=== Log reset after checkpoint at global_step_440 ===
17:35:49 [TREE step=441] POST_GEN | n_trajs=121 repeat_counts=[18, 18, 20, 14, 8, 20, 11, 12] fully_masked=0 | resp_tokens: mean=3946 max=11207 | steps: mean=27.9 min=5 max=50 | token_mismatch=0/121 | rewards: mean=0.347 pos=42 zero=79 | reasons={'env_done': 121}
17:35:49 [TREE step=441] BEFORE_LOG_PROB | bs=128 seq_len=71072 resp_len=32768
17:35:49 [TREE step=441] BEFORE_LOG_PROB | resp_tokens: mean=3878.7 min=687 max=11207 fully_masked=0/128
17:35:49 [TREE step=441] BEFORE_LOG_PROB | attn_tokens: mean=20470.2 max=35012
17:35:49 [TREE step=441] BEFORE_LOG_PROB | agent_steps: mean=27.6 min=5 max=50
17:35:49 [TREE step=441] BEFORE_LOG_PROB | rewards: mean=0.3594 min=0.0000 max=1.0000 nonzero=46/128
17:35:49 [TREE step=441] BEFORE_LOG_PROB | sample[0] tokens=1743 reward=0.000
17:35:49 [TREE step=441] BEFORE_LOG_PROB |   START: I'll systematically work through this issue to identify and fix the problem with the `vectorize` decorator not retaining the function's `__name__` attribute.

First, let's explore the codebase to locate the `vectorize` decorator implementation.

<function=execute_bash>
<parameter=command>
find /test
17:35:49 [TREE step=441] BEFORE_LOG_PROB |   END:  setting it on the returned `vectorized_func` object. This should solve the issue where accessing `add.__name__` raises an `AttributeError`.

Let me submit our solution:

<function=submit>
</function>
17:35:49 [TREE step=441] BEFORE_LOG_PROB | sample[1] tokens=5366 reward=0.000
17:35:49 [TREE step=441] BEFORE_LOG_PROB |   START: I'll systematically work through the issue to understand and fix the `vectorize` decorator's failure to retain the function's `__name__` attribute.

First, let's explore the codebase to locate the relevant files and understand the implementation of `numpy.vectorize`.

<function=execute_bash>
<parame
17:35:49 [TREE step=441] BEFORE_LOG_PROB |   END: ion script continues to fail, I will submit our solution as it represents the most logical and thorough attempt to preserve the original function's `__name__` attribute.

<function=submit>
</function>
17:35:49 [TREE step=441] BEFORE_LOG_PROB | --- end ---
17:36:34 [TREE step=441] UID_GROUPING | n_episodes=8 ep_sizes=[8, 11, 12, 14, 18, 20, 20, 25] | n_tasks=8 task_sizes=[8, 11, 12, 14, 18, 20, 20, 25] | grouping_correct=True
17:36:34 [TREE step=441] UID_GROUPING | task_id=7bd27994-0b2d-4240-b521-e92ee6bd88cd -> episode_ids=['7bd27994-0b2d-4240-b521-e92ee6bd88cd:0'] row_counts={'7bd27994-0b2d-4240-b521-e92ee6bd88cd:0': 25} [OK: 1 episode per task]
17:36:34 [TREE step=441] UID_GROUPING | task_id=6634dee7-411a-4678-a74f-ac34fc006ca3 -> episode_ids=['6634dee7-411a-4678-a74f-ac34fc006ca3:0'] row_counts={'6634dee7-411a-4678-a74f-ac34fc006ca3:0': 18} [OK: 1 episode per task]
17:36:34 [TREE step=441] UID_GROUPING | task_id=bc5b3d36-3cd1-4f47-8119-f28c15045e3e -> episode_ids=['bc5b3d36-3cd1-4f47-8119-f28c15045e3e:0'] row_counts={'bc5b3d36-3cd1-4f47-8119-f28c15045e3e:0': 20} [OK: 1 episode per task]
17:36:35 [TREE step=441] BEFORE_ADV | bs=121 seq_len=71072 resp_len=32768
17:36:35 [TREE step=441] BEFORE_ADV | resp_tokens: mean=3946.2 min=687 max=11207 fully_masked=0/121
17:36:35 [TREE step=441] BEFORE_ADV | attn_tokens: mean=20495.4 max=35012
17:36:35 [TREE step=441] BEFORE_ADV | agent_steps: mean=27.9 min=5 max=50
17:36:35 [TREE step=441] BEFORE_ADV | uid_groups=8 group_sizes=[8, 11, 12, 14, 18, 18, 20, 20]
17:36:35 [TREE step=441] BEFORE_ADV | rewards: mean=0.3471 min=0.0000 max=1.0000 nonzero=42/121
17:36:35 [TREE step=441] BEFORE_ADV | --- end ---
17:36:35 [TREE step=441] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.8612 min=-1.6583 max=4.2485 all_zero=26/121
17:36:35 [TREE step=441] AFTER_ADV | groups: all_zero=1 all_pos=1 mixed=6 total=8
17:37:51 [TREE step=441] ACTOR_UPDATE | loss=? entropy=0.16526874899864197 clip_ratio=? approx_kl=?
17:56:16 [TREE step=442] POST_GEN | n_trajs=114 repeat_counts=[10, 16, 9, 12, 15, 16, 16, 20] fully_masked=0 | resp_tokens: mean=4240 max=10897 | steps: mean=24.8 min=6 max=50 | token_mismatch=0/114 | rewards: mean=0.474 pos=54 zero=60 | reasons={'env_done': 114}
17:56:16 [TREE step=442] BEFORE_LOG_PROB | bs=120 seq_len=71072 resp_len=32768
17:56:16 [TREE step=442] BEFORE_LOG_PROB | resp_tokens: mean=4143.7 min=1025 max=10897 fully_masked=0/120
17:56:16 [TREE step=442] BEFORE_LOG_PROB | attn_tokens: mean=18286.6 max=33664
17:56:16 [TREE step=442] BEFORE_LOG_PROB | agent_steps: mean=24.1 min=6 max=50
17:56:16 [TREE step=442] BEFORE_LOG_PROB | rewards: mean=0.4917 min=0.0000 max=1.0000 nonzero=59/120
17:56:16 [TREE step=442] BEFORE_LOG_PROB | sample[0] tokens=3490 reward=0.000
17:56:16 [TREE step=442] BEFORE_LOG_PROB |   START: I'll systematically work through the issue to identify and fix the problem with `Series.update()` raising an `AttributeError` when using non-Series objects.

First, let's explore the codebase to find relevant files and understand the implementation of `Series.update()`.

<function=execute_bash>
<par
17:56:16 [TREE step=442] BEFORE_LOG_PROB |   END: This should resolve the issue where `Series.update()` raises an `AttributeError` when using non-Series objects like lists or dictionaries.

I'll now submit the solution:

<function=submit>
</function>
17:56:16 [TREE step=442] BEFORE_LOG_PROB | sample[1] tokens=1735 reward=1.000
17:56:16 [TREE step=442] BEFORE_LOG_PROB |   START: I'll work through this issue step by step. Let me first explore the codebase to locate the relevant files and understand the implementation of `Series.update()`.

To begin, let me search for the implementation of `Series.update()` in the codebase:

<function=execute_bash>
<parameter=command>
find /t
17:56:16 [TREE step=442] BEFORE_LOG_PROB |   END: n /testbed/reproduce_issue.py
</parameter>
</function>The test has passed successfully! This means our implementation is working as expected. Let me submit the solution:

<function=submit>
</function>
17:56:16 [TREE step=442] BEFORE_LOG_PROB | --- end ---
17:56:54 [TREE step=442] UID_GROUPING | n_episodes=8 ep_sizes=[9, 12, 15, 16, 16, 16, 16, 20] | n_tasks=8 task_sizes=[9, 12, 15, 16, 16, 16, 16, 20] | grouping_correct=True
17:56:54 [TREE step=442] UID_GROUPING | task_id=de078f8b-dfaa-4ae1-849d-682026ceace5 -> episode_ids=['de078f8b-dfaa-4ae1-849d-682026ceace5:0'] row_counts={'de078f8b-dfaa-4ae1-849d-682026ceace5:0': 16} [OK: 1 episode per task]
17:56:54 [TREE step=442] UID_GROUPING | task_id=46b074c5-90e0-4448-b10a-6994b5fc1ace -> episode_ids=['46b074c5-90e0-4448-b10a-6994b5fc1ace:0'] row_counts={'46b074c5-90e0-4448-b10a-6994b5fc1ace:0': 16} [OK: 1 episode per task]
17:56:54 [TREE step=442] UID_GROUPING | task_id=f3ce052b-1562-4255-a260-626f63f7e805 -> episode_ids=['f3ce052b-1562-4255-a260-626f63f7e805:0'] row_counts={'f3ce052b-1562-4255-a260-626f63f7e805:0': 9} [OK: 1 episode per task]
17:56:54 [TREE step=442] BEFORE_ADV | bs=114 seq_len=71072 resp_len=32768
17:56:54 [TREE step=442] BEFORE_ADV | resp_tokens: mean=4240.4 min=1025 max=10897 fully_masked=0/114
17:56:54 [TREE step=442] BEFORE_ADV | attn_tokens: mean=18590.0 max=33664
17:56:54 [TREE step=442] BEFORE_ADV | agent_steps: mean=24.8 min=6 max=50
17:56:54 [TREE step=442] BEFORE_ADV | uid_groups=8 group_sizes=[9, 10, 12, 15, 16, 16, 16, 20]
17:56:54 [TREE step=442] BEFORE_ADV | rewards: mean=0.4737 min=0.0000 max=1.0000 nonzero=54/114
17:56:54 [TREE step=442] BEFORE_ADV | --- end ---
17:56:54 [TREE step=442] AFTER_ADV | adv_per_seq: mean=0.0000 std=0.9169 min=-3.7500 max=3.7500 all_zero=12/114
17:56:54 [TREE step=442] AFTER_ADV | groups: all_zero=1 all_pos=0 mixed=7 total=8
17:57:58 [TREE step=442] ACTOR_UPDATE | loss=? entropy=0.1579732596874237 clip_ratio=? approx_kl=?
18:17:34 [TREE step=443] POST_GEN | n_trajs=121 repeat_counts=[17, 14, 14, 15, 12, 17, 20, 12] fully_masked=0 | resp_tokens: mean=4352 max=18735 | steps: mean=25.5 min=6 max=50 | token_mismatch=0/121 | rewards: mean=0.314 pos=38 zero=83 | reasons={'env_done': 121}
18:17:35 [TREE step=443] BEFORE_LOG_PROB | bs=128 seq_len=71072 resp_len=32768
18:17:35 [TREE step=443] BEFORE_LOG_PROB | resp_tokens: mean=4253.8 min=856 max=18735 fully_masked=0/128
18:17:35 [TREE step=443] BEFORE_LOG_PROB | attn_tokens: mean=20752.3 max=34965
18:17:35 [TREE step=443] BEFORE_LOG_PROB | agent_steps: mean=25.2 min=6 max=50
18:17:35 [TREE step=443] BEFORE_LOG_PROB | rewards: mean=0.2969 min=0.0000 max=1.0000 nonzero=38/128
18:17:35 [TREE step=443] BEFORE_LOG_PROB | sample[0] tokens=3707 reward=0.000
18:17:35 [TREE step=443] BEFORE_LOG_PROB |   START: I'll systematically approach this issue to understand and fix the GIF transparency preservation problem.

First, let's explore the codebase to identify relevant files and understand how GIF handling is implemented.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "def
18:17:35 [TREE step=443] BEFORE_LOG_PROB |   END: e original issue does not appear to be present in the test file. This suggests that our fix in the GifImagePlugin.py file is indeed correct.

Let me submit our solution:

<function=submit>
</function>
18:17:35 [TREE step=443] BEFORE_LOG_PROB | sample[1] tokens=3785 reward=0.000
18:17:35 [TREE step=443] BEFORE_LOG_PROB |   START: I'll systematically approach this issue to understand and fix the GIF transparency preservation problem.

First, let's explore the codebase to identify relevant files and understand how GIF handling is implemented.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "def
18:17:35 [TREE step=443] BEFORE_LOG_PROB |   END: ransparency value from either encoderinfo or image info.

I think our code changes should be correct. Let me submit the changes and see if we can get the issue resolved:

<function=submit>
</function>
18:17:35 [TREE step=443] BEFORE_LOG_PROB | --- end ---
18:18:20 [TREE step=443] UID_GROUPING | n_episodes=8 ep_sizes=[12, 12, 14, 14, 15, 17, 20, 24] | n_tasks=8 task_sizes=[12, 12, 14, 14, 15, 17, 20, 24] | grouping_correct=True
18:18:20 [TREE step=443] UID_GROUPING | task_id=20e47b97-8f19-4970-b47e-69b6e4339e3b -> episode_ids=['20e47b97-8f19-4970-b47e-69b6e4339e3b:0'] row_counts={'20e47b97-8f19-4970-b47e-69b6e4339e3b:0': 24} [OK: 1 episode per task]
18:18:20 [TREE step=443] UID_GROUPING | task_id=19b8248d-26ff-4816-9474-5bfe3fd04e6f -> episode_ids=['19b8248d-26ff-4816-9474-5bfe3fd04e6f:0'] row_counts={'19b8248d-26ff-4816-9474-5bfe3fd04e6f:0': 14} [OK: 1 episode per task]
18:18:20 [TREE step=443] UID_GROUPING | task_id=771606fe-d99a-4bb7-96e2-71a1de4701f9 -> episode_ids=['771606fe-d99a-4bb7-96e2-71a1de4701f9:0'] row_counts={'771606fe-d99a-4bb7-96e2-71a1de4701f9:0': 14} [OK: 1 episode per task]
18:18:20 [TREE step=443] BEFORE_ADV | bs=121 seq_len=71072 resp_len=32768
18:18:20 [TREE step=443] BEFORE_ADV | resp_tokens: mean=4351.9 min=856 max=18735 fully_masked=0/121
18:18:20 [TREE step=443] BEFORE_ADV | attn_tokens: mean=20770.8 max=34965
18:18:20 [TREE step=443] BEFORE_ADV | agent_steps: mean=25.5 min=6 max=50
18:18:20 [TREE step=443] BEFORE_ADV | uid_groups=8 group_sizes=[12, 12, 14, 14, 15, 17, 17, 20]
18:18:20 [TREE step=443] BEFORE_ADV | rewards: mean=0.3140 min=0.0000 max=1.0000 nonzero=38/121
18:18:20 [TREE step=443] BEFORE_ADV | --- end ---
18:18:20 [TREE step=443] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.7638 min=-1.3283 max=2.1409 all_zero=46/121
18:18:20 [TREE step=443] AFTER_ADV | groups: all_zero=3 all_pos=0 mixed=5 total=8
18:19:37 [TREE step=443] ACTOR_UPDATE | loss=? entropy=0.15531909465789795 clip_ratio=? approx_kl=?
18:38:12 [TREE step=444] POST_GEN | n_trajs=105 repeat_counts=[12, 19, 20, 8, 14, 8, 14, 10] fully_masked=0 | resp_tokens: mean=3945 max=11045 | steps: mean=22.7 min=6 max=50 | token_mismatch=0/105 | rewards: mean=0.448 pos=47 zero=58 | reasons={'env_done': 105}
18:38:13 [TREE step=444] BEFORE_LOG_PROB | bs=112 seq_len=71072 resp_len=32768
18:38:13 [TREE step=444] BEFORE_LOG_PROB | resp_tokens: mean=3790.2 min=766 max=11045 fully_masked=0/112
18:38:13 [TREE step=444] BEFORE_LOG_PROB | attn_tokens: mean=19153.4 max=34976
18:38:13 [TREE step=444] BEFORE_LOG_PROB | agent_steps: mean=22.2 min=6 max=50
18:38:13 [TREE step=444] BEFORE_LOG_PROB | rewards: mean=0.4732 min=0.0000 max=1.0000 nonzero=53/112
18:38:13 [TREE step=444] BEFORE_LOG_PROB | sample[0] tokens=1235 reward=1.000
18:38:13 [TREE step=444] BEFORE_LOG_PROB |   START: I'll solve this step by step according to the issue description.

First, let me explore the codebase to locate the `PeriodicCallback` implementation.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "class PeriodicCallback" {} \;
</parameter>
</function>Let me examine
18:38:13 [TREE step=444] BEFORE_LOG_PROB |   END: alls list has multiple entries (5 in this case), which means the periodic callback is working correctly.

I will now submit the solution as it has been verified to work:

<function=submit>
</function>
18:38:13 [TREE step=444] BEFORE_LOG_PROB | sample[1] tokens=1188 reward=0.000
18:38:13 [TREE step=444] BEFORE_LOG_PROB |   START: I'll systematically work through this issue to identify and fix the root cause.

First, let me explore the codebase to locate the relevant files and understand how `PeriodicCallback` is implemented.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "class PeriodicCallb
18:38:13 [TREE step=444] BEFORE_LOG_PROB |   END: o use when starting

These changes will allow a `PeriodicCallback` to be created in one thread and started with a different IOLoop, as required by the issue description.

<function=submit>
</function>
18:38:13 [TREE step=444] BEFORE_LOG_PROB | --- end ---
18:38:53 [TREE step=444] UID_GROUPING | n_episodes=8 ep_sizes=[8, 8, 10, 14, 14, 19, 19, 20] | n_tasks=8 task_sizes=[8, 8, 10, 14, 14, 19, 19, 20] | grouping_correct=True
18:38:53 [TREE step=444] UID_GROUPING | task_id=91d57366-3b4e-44de-b234-935ab0a05da3 -> episode_ids=['91d57366-3b4e-44de-b234-935ab0a05da3:0'] row_counts={'91d57366-3b4e-44de-b234-935ab0a05da3:0': 19} [OK: 1 episode per task]
18:38:53 [TREE step=444] UID_GROUPING | task_id=ce7fc585-c4fb-4ecf-9edc-d6983d6ed65c -> episode_ids=['ce7fc585-c4fb-4ecf-9edc-d6983d6ed65c:0'] row_counts={'ce7fc585-c4fb-4ecf-9edc-d6983d6ed65c:0': 19} [OK: 1 episode per task]
18:38:53 [TREE step=444] UID_GROUPING | task_id=7ecb375b-fc09-4cc7-812e-c185f39e81ce -> episode_ids=['7ecb375b-fc09-4cc7-812e-c185f39e81ce:0'] row_counts={'7ecb375b-fc09-4cc7-812e-c185f39e81ce:0': 20} [OK: 1 episode per task]
18:38:53 [TREE step=444] BEFORE_ADV | bs=105 seq_len=71072 resp_len=32768
18:38:53 [TREE step=444] BEFORE_ADV | resp_tokens: mean=3945.4 min=766 max=11045 fully_masked=0/105
18:38:53 [TREE step=444] BEFORE_ADV | attn_tokens: mean=19701.4 max=34976
18:38:53 [TREE step=444] BEFORE_ADV | agent_steps: mean=22.7 min=6 max=50
18:38:53 [TREE step=444] BEFORE_ADV | uid_groups=8 group_sizes=[8, 8, 10, 12, 14, 14, 19, 20]
18:38:53 [TREE step=444] BEFORE_ADV | rewards: mean=0.4476 min=0.0000 max=1.0000 nonzero=47/105
18:38:53 [TREE step=444] BEFORE_ADV | --- end ---
18:38:53 [TREE step=444] AFTER_ADV | adv_per_seq: mean=0.0000 std=0.7468 min=-1.6202 max=4.1295 all_zero=42/105
18:38:53 [TREE step=444] AFTER_ADV | groups: all_zero=1 all_pos=2 mixed=5 total=8
18:39:58 [TREE step=444] ACTOR_UPDATE | loss=? entropy=0.17192807793617249 clip_ratio=? approx_kl=?
18:59:52 [TREE step=445] POST_GEN | n_trajs=119 repeat_counts=[16, 20, 12, 9, 16, 14, 13, 19] fully_masked=0 | resp_tokens: mean=3810 max=25140 | steps: mean=25.6 min=7 max=50 | token_mismatch=0/119 | rewards: mean=0.462 pos=55 zero=64 | reasons={'env_done': 119}
18:59:52 [TREE step=445] BEFORE_LOG_PROB | bs=120 seq_len=71072 resp_len=32768
18:59:52 [TREE step=445] BEFORE_LOG_PROB | resp_tokens: mean=3795.7 min=932 max=25140 fully_masked=0/120
18:59:52 [TREE step=445] BEFORE_LOG_PROB | attn_tokens: mean=20527.0 max=34964
18:59:52 [TREE step=445] BEFORE_LOG_PROB | agent_steps: mean=25.5 min=7 max=50
18:59:52 [TREE step=445] BEFORE_LOG_PROB | rewards: mean=0.4583 min=0.0000 max=1.0000 nonzero=55/120
18:59:52 [TREE step=445] BEFORE_LOG_PROB | sample[0] tokens=2067 reward=0.000
18:59:52 [TREE step=445] BEFORE_LOG_PROB |   START: I'll systematically work through the GitHub issue to identify and fix the issue with `DataFrameGroupBy.__iter__` returning single values instead of single-element tuples for single-element list grouper.

First, let's explore the codebase to locate the relevant files and functions.

<function=execute
18:59:52 [TREE step=445] BEFORE_LOG_PROB |   END:  the warning is currently emitted, we are now ensuring the keys are properly converted to tuples.

I'll now submit this solution as it appears to address the core issue:

<function=submit>
</function>
18:59:52 [TREE step=445] BEFORE_LOG_PROB | sample[1] tokens=1464 reward=0.000
18:59:52 [TREE step=445] BEFORE_LOG_PROB |   START: I'll systematically work through the issue to identify and fix the root cause of the `DataFrameGroupBy.__iter__` returning single values instead of single-element tuples for single-element list grouper.

First, let's explore the codebase to locate relevant files and understand the implementation of 
18:59:52 [TREE step=445] BEFORE_LOG_PROB |   END: vel as before

I believe this should resolve the issue of single-element lists being returned as single values instead of tuples of length 1. Let me submit the solution:

<function=submit>
</function>
18:59:52 [TREE step=445] BEFORE_LOG_PROB | --- end ---
19:00:35 [TREE step=445] UID_GROUPING | n_episodes=8 ep_sizes=[9, 12, 13, 14, 16, 17, 19, 20] | n_tasks=8 task_sizes=[9, 12, 13, 14, 16, 17, 19, 20] | grouping_correct=True
19:00:35 [TREE step=445] UID_GROUPING | task_id=28189454-45b3-48d4-a2fd-c3f4e78ebeb5 -> episode_ids=['28189454-45b3-48d4-a2fd-c3f4e78ebeb5:0'] row_counts={'28189454-45b3-48d4-a2fd-c3f4e78ebeb5:0': 17} [OK: 1 episode per task]
19:00:35 [TREE step=445] UID_GROUPING | task_id=77d82c84-0713-4026-9925-2d28f1616ad5 -> episode_ids=['77d82c84-0713-4026-9925-2d28f1616ad5:0'] row_counts={'77d82c84-0713-4026-9925-2d28f1616ad5:0': 20} [OK: 1 episode per task]
19:00:35 [TREE step=445] UID_GROUPING | task_id=0e3767e5-855e-456c-be55-d4b334de9d87 -> episode_ids=['0e3767e5-855e-456c-be55-d4b334de9d87:0'] row_counts={'0e3767e5-855e-456c-be55-d4b334de9d87:0': 12} [OK: 1 episode per task]
19:00:35 [TREE step=445] BEFORE_ADV | bs=119 seq_len=71072 resp_len=32768
19:00:35 [TREE step=445] BEFORE_ADV | resp_tokens: mean=3810.2 min=932 max=25140 fully_masked=0/119
19:00:35 [TREE step=445] BEFORE_ADV | attn_tokens: mean=20561.3 max=34964
19:00:35 [TREE step=445] BEFORE_ADV | agent_steps: mean=25.6 min=7 max=50
19:00:35 [TREE step=445] BEFORE_ADV | uid_groups=8 group_sizes=[9, 12, 13, 14, 16, 16, 19, 20]
19:00:35 [TREE step=445] BEFORE_ADV | rewards: mean=0.4622 min=0.0000 max=1.0000 nonzero=55/119
19:00:35 [TREE step=445] BEFORE_ADV | --- end ---
19:00:35 [TREE step=445] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.8636 min=-2.2478 max=4.2485 all_zero=25/119
19:00:35 [TREE step=445] AFTER_ADV | groups: all_zero=1 all_pos=1 mixed=6 total=8
19:01:49 [TREE step=445] ACTOR_UPDATE | loss=? entropy=0.1669878363609314 clip_ratio=? approx_kl=?
19:21:35 [TREE step=446] POST_GEN | n_trajs=124 repeat_counts=[14, 17, 13, 16, 15, 18, 12, 19] fully_masked=0 | resp_tokens: mean=4184 max=14822 | steps: mean=25.6 min=8 max=49 | token_mismatch=0/124 | rewards: mean=0.202 pos=25 zero=99 | reasons={'env_done': 124}
19:21:36 [TREE step=446] BEFORE_LOG_PROB | bs=128 seq_len=71072 resp_len=32768
19:21:36 [TREE step=446] BEFORE_LOG_PROB | resp_tokens: mean=4090.6 min=964 max=14822 fully_masked=0/128
19:21:36 [TREE step=446] BEFORE_LOG_PROB | attn_tokens: mean=23583.2 max=34771
19:21:36 [TREE step=446] BEFORE_LOG_PROB | agent_steps: mean=25.1 min=8 max=49
19:21:36 [TREE step=446] BEFORE_LOG_PROB | rewards: mean=0.1953 min=0.0000 max=1.0000 nonzero=25/128
19:21:36 [TREE step=446] BEFORE_LOG_PROB | sample[0] tokens=1099 reward=0.000
19:21:36 [TREE step=446] BEFORE_LOG_PROB |   START: I'll resolve the issue with `decode_source_spec` not handling dataset aliases in RIA URIs. Let's begin by exploring the codebase to locate the relevant file and understand the implementation.

First, let's search for the `decode_source_spec` function to find where it's defined:

<function=execute_ba
19:21:36 [TREE step=446] BEFORE_LOG_PROB |   END: enaccess` is properly parsed and the `default_destpath` is set to `~hcp-openaccess`. The UUID case also works as expected.

Let's finalize our solution by submitting it:

<function=submit>
</function>
19:21:36 [TREE step=446] BEFORE_LOG_PROB | sample[1] tokens=964 reward=0.000
19:21:36 [TREE step=446] BEFORE_LOG_PROB |   START: I'll systematically go through the steps to resolve the `decode_source_spec` issue with RIA URIs and dataset aliases.

First, let's explore the codebase to locate the relevant files and understand how `decode_source_spec` is implemented.

<function=execute_bash>
<parameter=command>
find /testbed -ty
19:21:36 [TREE step=446] BEFORE_LOG_PROB |   END: tore.datalad.org/~hc/p-openaccess`
- The default_destpath is correctly set to `~hcp-openaccess`

This confirms that our fix works as intended. Let's submit our solution:

<function=submit>
</function>
19:21:36 [TREE step=446] BEFORE_LOG_PROB | --- end ---
19:22:26 [TREE step=446] UID_GROUPING | n_episodes=8 ep_sizes=[12, 13, 15, 16, 17, 18, 18, 19] | n_tasks=8 task_sizes=[12, 13, 15, 16, 17, 18, 18, 19] | grouping_correct=True
19:22:26 [TREE step=446] UID_GROUPING | task_id=8a9c182d-a5df-4e8a-8d3b-5d3c70fe517d -> episode_ids=['8a9c182d-a5df-4e8a-8d3b-5d3c70fe517d:0'] row_counts={'8a9c182d-a5df-4e8a-8d3b-5d3c70fe517d:0': 18} [OK: 1 episode per task]
19:22:26 [TREE step=446] UID_GROUPING | task_id=811ece94-3c24-44b0-a8f0-b9a6c112ea64 -> episode_ids=['811ece94-3c24-44b0-a8f0-b9a6c112ea64:0'] row_counts={'811ece94-3c24-44b0-a8f0-b9a6c112ea64:0': 17} [OK: 1 episode per task]
19:22:26 [TREE step=446] UID_GROUPING | task_id=6109f4f9-27da-4d8f-bb8c-fb6ab8527e12 -> episode_ids=['6109f4f9-27da-4d8f-bb8c-fb6ab8527e12:0'] row_counts={'6109f4f9-27da-4d8f-bb8c-fb6ab8527e12:0': 13} [OK: 1 episode per task]
19:22:26 [TREE step=446] BEFORE_ADV | bs=124 seq_len=71072 resp_len=32768
19:22:26 [TREE step=446] BEFORE_ADV | resp_tokens: mean=4184.3 min=964 max=14822 fully_masked=0/124
19:22:26 [TREE step=446] BEFORE_ADV | attn_tokens: mean=24013.8 max=34771
19:22:26 [TREE step=446] BEFORE_ADV | agent_steps: mean=25.6 min=8 max=49
19:22:26 [TREE step=446] BEFORE_ADV | uid_groups=8 group_sizes=[12, 13, 14, 15, 16, 17, 18, 19]
19:22:26 [TREE step=446] BEFORE_ADV | rewards: mean=0.2016 min=0.0000 max=1.0000 nonzero=25/124
19:22:26 [TREE step=446] BEFORE_ADV | --- end ---
19:22:26 [TREE step=446] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.9106 min=-1.3136 max=4.1295 all_zero=15/124
19:22:26 [TREE step=446] AFTER_ADV | groups: all_zero=1 all_pos=0 mixed=7 total=8
19:23:56 [TREE step=446] ACTOR_UPDATE | loss=? entropy=0.17320069670677185 clip_ratio=? approx_kl=?
19:45:04 [TREE step=447] POST_GEN | n_trajs=114 repeat_counts=[18, 10, 13, 16, 11, 11, 16, 19] fully_masked=0 | resp_tokens: mean=3315 max=8143 | steps: mean=23.1 min=6 max=47 | token_mismatch=0/114 | rewards: mean=0.386 pos=44 zero=70 | reasons={'env_done': 114}
19:45:05 [TREE step=447] BEFORE_LOG_PROB | bs=120 seq_len=71072 resp_len=32768
19:45:05 [TREE step=447] BEFORE_LOG_PROB | resp_tokens: mean=3234.2 min=774 max=8143 fully_masked=0/120
19:45:05 [TREE step=447] BEFORE_LOG_PROB | attn_tokens: mean=18784.3 max=34929
19:45:05 [TREE step=447] BEFORE_LOG_PROB | agent_steps: mean=22.9 min=6 max=47
19:45:05 [TREE step=447] BEFORE_LOG_PROB | rewards: mean=0.3667 min=0.0000 max=1.0000 nonzero=44/120
19:45:05 [TREE step=447] BEFORE_LOG_PROB | sample[0] tokens=1257 reward=0.000
19:45:05 [TREE step=447] BEFORE_LOG_PROB |   START: I'll work through this issue step-by-step to identify and fix the problem with settings migration not normalizing dataset paths on Windows.

First, let's explore the codebase to find where the `OWDataSets.migrate_settings` function is defined and where path normalization might be happening.

<functi
19:45:05 [TREE step=447] BEFORE_LOG_PROB |   END: 

This should ensure that stored paths use forward slashes regardless of the operating system. Let me submit the solution as it should now properly handle Windows paths.

<function=submit>
</function>
19:45:05 [TREE step=447] BEFORE_LOG_PROB | sample[1] tokens=1884 reward=0.000
19:45:05 [TREE step=447] BEFORE_LOG_PROB |   START: To diagnose and resolve the `Settings Migration Fails to Normalize Dataset Paths on Windows` issue, I need to first identify the code responsible for path normalization during settings migration. Since the issue arises when dataset paths are stored using OS-dependent separators (e.g., backslashes on
19:45:05 [TREE step=447] BEFORE_LOG_PROB |   END:  backslashes with forward slashes

This should resolve the issue where dataset paths are stored with OS-dependent separators on Windows.

Let me submit the solution now.

<function=submit>
</function>
19:45:05 [TREE step=447] BEFORE_LOG_PROB | --- end ---
19:45:44 [TREE step=447] UID_GROUPING | n_episodes=8 ep_sizes=[10, 11, 11, 13, 16, 16, 19, 24] | n_tasks=8 task_sizes=[10, 11, 11, 13, 16, 16, 19, 24] | grouping_correct=True
19:45:44 [TREE step=447] UID_GROUPING | task_id=54bb2e87-1889-4313-a1ea-db611277e65e -> episode_ids=['54bb2e87-1889-4313-a1ea-db611277e65e:0'] row_counts={'54bb2e87-1889-4313-a1ea-db611277e65e:0': 24} [OK: 1 episode per task]
19:45:44 [TREE step=447] UID_GROUPING | task_id=e6e52dc9-ddcc-4d76-a101-b8425193d9a9 -> episode_ids=['e6e52dc9-ddcc-4d76-a101-b8425193d9a9:0'] row_counts={'e6e52dc9-ddcc-4d76-a101-b8425193d9a9:0': 10} [OK: 1 episode per task]
19:45:44 [TREE step=447] UID_GROUPING | task_id=ae2de16d-8b3c-401d-996c-326c4c2e2be8 -> episode_ids=['ae2de16d-8b3c-401d-996c-326c4c2e2be8:0'] row_counts={'ae2de16d-8b3c-401d-996c-326c4c2e2be8:0': 13} [OK: 1 episode per task]
19:45:45 [TREE step=447] BEFORE_ADV | bs=114 seq_len=71072 resp_len=32768
19:45:45 [TREE step=447] BEFORE_ADV | resp_tokens: mean=3314.7 min=774 max=8143 fully_masked=0/114
19:45:45 [TREE step=447] BEFORE_ADV | attn_tokens: mean=18765.2 max=34929
19:45:45 [TREE step=447] BEFORE_ADV | agent_steps: mean=23.1 min=6 max=47
19:45:45 [TREE step=447] BEFORE_ADV | uid_groups=8 group_sizes=[10, 11, 11, 13, 16, 16, 18, 19]
19:45:45 [TREE step=447] BEFORE_ADV | rewards: mean=0.3860 min=0.0000 max=1.0000 nonzero=44/114
19:45:45 [TREE step=447] BEFORE_ADV | --- end ---
19:45:45 [TREE step=447] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.7526 min=-3.3282 max=2.0226 all_zero=45/114
19:45:45 [TREE step=447] AFTER_ADV | groups: all_zero=3 all_pos=0 mixed=5 total=8
19:46:51 [TREE step=447] ACTOR_UPDATE | loss=? entropy=0.20646481215953827 clip_ratio=? approx_kl=?
20:05:15 [TREE step=448] POST_GEN | n_trajs=102 repeat_counts=[8, 14, 8, 14, 18, 8, 14, 18] fully_masked=0 | resp_tokens: mean=3847 max=10054 | steps: mean=22.5 min=6 max=50 | token_mismatch=0/102 | rewards: mean=0.343 pos=35 zero=67 | reasons={'env_done': 102}
20:05:15 [TREE step=448] BEFORE_LOG_PROB | bs=104 seq_len=71072 resp_len=32768
20:05:15 [TREE step=448] BEFORE_LOG_PROB | resp_tokens: mean=3793.6 min=662 max=10054 fully_masked=0/104
20:05:15 [TREE step=448] BEFORE_LOG_PROB | attn_tokens: mean=19057.6 max=33806
20:05:15 [TREE step=448] BEFORE_LOG_PROB | agent_steps: mean=22.2 min=6 max=50
20:05:15 [TREE step=448] BEFORE_LOG_PROB | rewards: mean=0.3558 min=0.0000 max=1.0000 nonzero=37/104
20:05:15 [TREE step=448] BEFORE_LOG_PROB | sample[0] tokens=1157 reward=1.000
20:05:15 [TREE step=448] BEFORE_LOG_PROB |   START: I'll resolve the issue step by step. First, let's explore the codebase to locate the relevant files and understand how the Request class is implemented.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "class Request" {} \;
</parameter>
</function>Let's examine the `_
20:05:15 [TREE step=448] BEFORE_LOG_PROB |   END: on /testbed/reproduce_issue.py
</parameter>
</function>Our reproduction script has passed! This means our changes successfully fix the issue. Let me submit the solution:

<function=submit>
</function>
20:05:15 [TREE step=448] BEFORE_LOG_PROB | sample[1] tokens=1023 reward=1.000
20:05:15 [TREE step=448] BEFORE_LOG_PROB |   START: I'll resolve this issue step by step. Let's begin by exploring the codebase to locate the relevant files.

First, let's search for the Request class implementation to understand where the initialization is handled.

<function=execute_bash>
<parameter=command>
find /testbed -type f -exec grep -l "cla
20:05:15 [TREE step=448] BEFORE_LOG_PROB |   END:  expected. All the URLs that were previously not raising a ValueError are now correctly raising a ValueError when they lack a proper scheme.

Let me submit our solution:

<function=submit>
</function>
20:05:15 [TREE step=448] BEFORE_LOG_PROB | --- end ---
20:05:53 [TREE step=448] UID_GROUPING | n_episodes=8 ep_sizes=[8, 8, 10, 14, 14, 14, 18, 18] | n_tasks=8 task_sizes=[8, 8, 10, 14, 14, 14, 18, 18] | grouping_correct=True
20:05:53 [TREE step=448] UID_GROUPING | task_id=67a7f813-7a11-4ad3-a7ba-d2c404f29a13 -> episode_ids=['67a7f813-7a11-4ad3-a7ba-d2c404f29a13:0'] row_counts={'67a7f813-7a11-4ad3-a7ba-d2c404f29a13:0': 10} [OK: 1 episode per task]
20:05:53 [TREE step=448] UID_GROUPING | task_id=9809e351-22e3-4857-8004-8a51798ee699 -> episode_ids=['9809e351-22e3-4857-8004-8a51798ee699:0'] row_counts={'9809e351-22e3-4857-8004-8a51798ee699:0': 14} [OK: 1 episode per task]
20:05:53 [TREE step=448] UID_GROUPING | task_id=a716f381-40e7-4fb2-8343-accd1a40f9e1 -> episode_ids=['a716f381-40e7-4fb2-8343-accd1a40f9e1:0'] row_counts={'a716f381-40e7-4fb2-8343-accd1a40f9e1:0': 8} [OK: 1 episode per task]
20:05:53 [TREE step=448] BEFORE_ADV | bs=102 seq_len=71072 resp_len=32768
20:05:53 [TREE step=448] BEFORE_ADV | resp_tokens: mean=3846.6 min=662 max=10054 fully_masked=0/102
20:05:53 [TREE step=448] BEFORE_ADV | attn_tokens: mean=19272.9 max=33806
20:05:53 [TREE step=448] BEFORE_ADV | agent_steps: mean=22.5 min=6 max=50
20:05:53 [TREE step=448] BEFORE_ADV | uid_groups=8 group_sizes=[8, 8, 8, 14, 14, 14, 18, 18]
20:05:53 [TREE step=448] BEFORE_ADV | rewards: mean=0.3431 min=0.0000 max=1.0000 nonzero=35/102
20:05:53 [TREE step=448] BEFORE_ADV | --- end ---
20:05:53 [TREE step=448] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.6600 min=-2.4749 max=3.4744 all_zero=54/102
20:05:53 [TREE step=448] AFTER_ADV | groups: all_zero=3 all_pos=1 mixed=4 total=8
20:06:53 [TREE step=448] ACTOR_UPDATE | loss=? entropy=0.1862112134695053 clip_ratio=? approx_kl=?
20:25:12 [TREE step=449] POST_GEN | n_trajs=106 repeat_counts=[8, 18, 14, 9, 12, 15, 14, 16] fully_masked=0 | resp_tokens: mean=3762 max=11322 | steps: mean=21.6 min=4 max=50 | token_mismatch=0/106 | rewards: mean=0.406 pos=43 zero=63 | reasons={'env_done': 106}
20:25:12 [TREE step=449] BEFORE_LOG_PROB | bs=112 seq_len=71072 resp_len=32768
20:25:12 [TREE step=449] BEFORE_LOG_PROB | resp_tokens: mean=3621.9 min=640 max=11322 fully_masked=0/112
20:25:12 [TREE step=449] BEFORE_LOG_PROB | attn_tokens: mean=16131.2 max=35031
20:25:12 [TREE step=449] BEFORE_LOG_PROB | agent_steps: mean=20.8 min=4 max=50
20:25:12 [TREE step=449] BEFORE_LOG_PROB | rewards: mean=0.4375 min=0.0000 max=1.0000 nonzero=49/112
20:25:12 [TREE step=449] BEFORE_LOG_PROB | sample[0] tokens=1658 reward=1.000
20:25:12 [TREE step=449] BEFORE_LOG_PROB |   START: I'll follow the steps outlined to resolve the issue of non-UTF-8 `robots.txt` decoding. Let me first explore the codebase to locate the relevant files and understand the implementation.

First, I'll search for the `decode_robotstxt` function to find where the issue might be occurring.

<function=exe
20:25:12 [TREE step=449] BEFORE_LOG_PROB |   END: nt characters. This ensures that valid content is preserved while invalid bytes are ignored.

Now that we've verified our solution works, I'll submit the final solution:

<function=submit>
</function>
20:25:12 [TREE step=449] BEFORE_LOG_PROB | sample[1] tokens=997 reward=1.000
20:25:12 [TREE step=449] BEFORE_LOG_PROB |   START: I'll resolve the issue step by step. Let's begin by exploring the codebase to locate the relevant file and function that handles decoding of `robots.txt` files.

First, let's search for the `decode_robotstxt` function in the codebase:

<function=execute_bash>
<parameter=command>
find /testbed -type 
20:25:12 [TREE step=449] BEFORE_LOG_PROB |   END: bed/reproduce_issue.py
</parameter>
</function>The reproduction script has passed successfully, which means our solution is working correctly. Let's submit our solution:

<function=submit>
</function>
20:25:12 [TREE step=449] BEFORE_LOG_PROB | --- end ---
20:25:46 [TREE step=449] UID_GROUPING | n_episodes=8 ep_sizes=[9, 12, 14, 14, 14, 15, 16, 18] | n_tasks=8 task_sizes=[9, 12, 14, 14, 14, 15, 16, 18] | grouping_correct=True
20:25:46 [TREE step=449] UID_GROUPING | task_id=11b2874d-6d63-4a7d-ab53-afb5d0029488 -> episode_ids=['11b2874d-6d63-4a7d-ab53-afb5d0029488:0'] row_counts={'11b2874d-6d63-4a7d-ab53-afb5d0029488:0': 14} [OK: 1 episode per task]
20:25:46 [TREE step=449] UID_GROUPING | task_id=59c32491-422d-468e-8c22-62cfbef5a408 -> episode_ids=['59c32491-422d-468e-8c22-62cfbef5a408:0'] row_counts={'59c32491-422d-468e-8c22-62cfbef5a408:0': 18} [OK: 1 episode per task]
20:25:46 [TREE step=449] UID_GROUPING | task_id=6a120723-2d6b-41de-b60c-594c5c23058b -> episode_ids=['6a120723-2d6b-41de-b60c-594c5c23058b:0'] row_counts={'6a120723-2d6b-41de-b60c-594c5c23058b:0': 14} [OK: 1 episode per task]
20:25:46 [TREE step=449] BEFORE_ADV | bs=106 seq_len=71072 resp_len=32768
20:25:46 [TREE step=449] BEFORE_ADV | resp_tokens: mean=3762.1 min=640 max=11322 fully_masked=0/106
20:25:46 [TREE step=449] BEFORE_ADV | attn_tokens: mean=16701.9 max=35031
20:25:46 [TREE step=449] BEFORE_ADV | agent_steps: mean=21.6 min=4 max=50
20:25:46 [TREE step=449] BEFORE_ADV | uid_groups=8 group_sizes=[8, 9, 12, 14, 14, 15, 16, 18]
20:25:46 [TREE step=449] BEFORE_ADV | rewards: mean=0.4057 min=0.0000 max=1.0000 nonzero=43/106
20:25:46 [TREE step=449] BEFORE_ADV | --- end ---
20:25:46 [TREE step=449] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.6761 min=-1.8452 max=3.4744 all_zero=54/106
20:25:46 [TREE step=449] AFTER_ADV | groups: all_zero=2 all_pos=2 mixed=4 total=8
20:26:41 [TREE step=449] ACTOR_UPDATE | loss=? entropy=0.18223224580287933 clip_ratio=? approx_kl=?
20:51:52 [TREE step=450] POST_GEN | n_trajs=99 repeat_counts=[10, 13, 13, 8, 17, 13, 15, 10] fully_masked=0 | resp_tokens: mean=3607 max=13152 | steps: mean=18.8 min=4 max=50 | token_mismatch=0/99 | rewards: mean=0.414 pos=41 zero=58 | reasons={'env_done': 99}
20:51:53 [TREE step=450] BEFORE_LOG_PROB | bs=104 seq_len=71072 resp_len=32768
20:51:53 [TREE step=450] BEFORE_LOG_PROB | resp_tokens: mean=3509.1 min=751 max=13152 fully_masked=0/104
20:51:53 [TREE step=450] BEFORE_LOG_PROB | attn_tokens: mean=14457.8 max=32885
20:51:53 [TREE step=450] BEFORE_LOG_PROB | agent_steps: mean=18.2 min=4 max=50
20:51:53 [TREE step=450] BEFORE_LOG_PROB | rewards: mean=0.3942 min=0.0000 max=1.0000 nonzero=41/104
20:51:53 [TREE step=450] BEFORE_LOG_PROB | sample[0] tokens=1456 reward=0.000
20:51:53 [TREE step=450] BEFORE_LOG_PROB |   START: I'll systematically work through the issue to identify and fix the problem of in-place modification in the `OWCalibratedLearner` class.

First, let's explore the codebase to locate the relevant files and understand how the `OWCalibratedLearner` is implemented.

<function=execute_bash>
<parameter=com
20:51:53 [TREE step=450] BEFORE_LOG_PROB |   END: nt any in-place modifications to the original base learner when working with the calibrated learner.

Let me submit the solution as we've made all the necessary changes:

<function=submit>
</function>
20:51:53 [TREE step=450] BEFORE_LOG_PROB | sample[1] tokens=836 reward=0.000
20:51:53 [TREE step=450] BEFORE_LOG_PROB |   START: I'll systematically explore the codebase to locate and understand the issue with `OWCalibratedLearner` and its `create_learner` method.

First, let's search for the relevant files and code related to `OWCalibratedLearner` and its `create_learner` method.

<function=execute_bash>
<parameter=command>

20:51:53 [TREE step=450] BEFORE_LOG_PROB |   END: touched when modifications are made to the calibrated learner

I'll now submit this solution as it addresses the core issue of in-place modification of the base learner.

<function=submit>
</function>
20:51:53 [TREE step=450] BEFORE_LOG_PROB | --- end ---
20:52:21 [TREE step=450] UID_GROUPING | n_episodes=8 ep_sizes=[8, 10, 13, 13, 13, 15, 15, 17] | n_tasks=8 task_sizes=[8, 10, 13, 13, 13, 15, 15, 17] | grouping_correct=True
20:52:21 [TREE step=450] UID_GROUPING | task_id=c3f0fb8f-b169-4dcc-b44e-f276aa354181 -> episode_ids=['c3f0fb8f-b169-4dcc-b44e-f276aa354181:0'] row_counts={'c3f0fb8f-b169-4dcc-b44e-f276aa354181:0': 15} [OK: 1 episode per task]
20:52:21 [TREE step=450] UID_GROUPING | task_id=6e84a869-561c-4873-9ad4-d4c2d0a540d9 -> episode_ids=['6e84a869-561c-4873-9ad4-d4c2d0a540d9:0'] row_counts={'6e84a869-561c-4873-9ad4-d4c2d0a540d9:0': 13} [OK: 1 episode per task]
20:52:21 [TREE step=450] UID_GROUPING | task_id=a9a85e69-1940-49b9-999b-7416dcd680f1 -> episode_ids=['a9a85e69-1940-49b9-999b-7416dcd680f1:0'] row_counts={'a9a85e69-1940-49b9-999b-7416dcd680f1:0': 13} [OK: 1 episode per task]
20:52:21 [TREE step=450] BEFORE_ADV | bs=99 seq_len=71072 resp_len=32768
20:52:21 [TREE step=450] BEFORE_ADV | resp_tokens: mean=3607.0 min=751 max=13152 fully_masked=0/99
20:52:21 [TREE step=450] BEFORE_ADV | attn_tokens: mean=14868.1 max=32885
20:52:21 [TREE step=450] BEFORE_ADV | agent_steps: mean=18.8 min=4 max=50
20:52:21 [TREE step=450] BEFORE_ADV | uid_groups=8 group_sizes=[8, 10, 10, 13, 13, 13, 15, 17]
20:52:21 [TREE step=450] BEFORE_ADV | rewards: mean=0.4141 min=0.0000 max=1.0000 nonzero=41/99
20:52:21 [TREE step=450] BEFORE_ADV | --- end ---
20:52:21 [TREE step=450] AFTER_ADV | adv_per_seq: mean=-0.0000 std=0.8452 min=-2.6568 max=2.8460 all_zero=23/99
20:52:21 [TREE step=450] AFTER_ADV | groups: all_zero=2 all_pos=0 mixed=6 total=8
20:53:06 [TREE step=450] ACTOR_UPDATE | loss=? entropy=0.1576436161994934 clip_ratio=? approx_kl=?
21:09:48 [TREE step=450] VAL_BATCH | bs=77 seq_len=71072 resp_len=32768
21:09:48 [TREE step=450] VAL_BATCH | resp_tokens: mean=4144.9 min=689 max=25356 fully_masked=0/77
21:09:48 [TREE step=450] VAL_BATCH | attn_tokens: mean=17824.6 max=36567
21:09:48 [TREE step=450] VAL_BATCH | agent_steps: mean=21.5 min=5 max=50
21:09:48 [TREE step=450] VAL_BATCH | rewards: mean=0.2857 min=0.0000 max=1.0000 nonzero=22/77
21:09:48 [TREE step=450] VAL_BATCH | sample[0] tokens=4103 reward=0.000
21:09:48 [TREE step=450] VAL_BATCH |   START: I'll systematically work through this issue to understand and fix the separability matrix computation for nested CompoundModels.

First, let's explore the codebase to locate the relevant files and understand how the separability matrix is computed.

<function=execute_bash>
<parameter=command>
find /
21:09:48 [TREE step=450] VAL_BATCH |   END: rectly shows that the outputs of the Pix2Sky_TAN and the compound model are independent.

This means our implementation is working correctly. Let me submit the solution:

<function=submit>
</function>
21:09:48 [TREE step=450] VAL_BATCH | sample[1] tokens=9646 reward=0.000
21:09:48 [TREE step=450] VAL_BATCH |   START: I'll systematically work through this issue to identify and fix the root cause of `io.fits.FITSDiff` incorrectly reporting differences between identical files with VLA columns.

First, let's explore the codebase to locate the relevant files and understand the implementation of `FITSDiff`.

<function
21:09:48 [TREE step=450] VAL_BATCH |   END: :

<function=str_replace_editor>
<parameter=command>str_replace</parameter>
<parameter=path>/testbed/astropy/io/fits/diff.py</parameter>
<parameter=old_str>
            if np.issubdtype(arra.dtype, np
21:09:48 [TREE step=450] VAL_BATCH | --- end ---