The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label Chimpanzee-1.1-Dataset@14d6ed5f69c3398f52edc14732319911fe575e9a
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label Chimpanzee-1.1-Dataset@14d6ed5f69c3398f52edc14732319911fe575e9aNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Chimpanzee-1.1 Dataset
Chimpanzee-1.1 uses a four-stage multimodal DPO curriculum containing 580 preference pairs and 580 corresponding 512×512 PNG frames:
- Stage 1: 75 ARC-Witness Level 1 examples.
- Stage 1C: 75 ARC-Interactive Level 1 examples.
- Stage 2: 144 ARC-Witness later-level examples.
- Stage 2C: 286 ARC-Interactive examples from Levels 2 and later.
The chosen trajectories were generated by Kimi K3 solving normally without receiving the correct actions. Each chosen completion contains a compact reasoning summary followed by a replay-verified correct Python action call. Raw Kimi chain of thought is excluded.
Rejected completions were generated by Qwen3-VL-8B-Instruct without access to the correct action and with thinking disabled. Each chosen and rejected branch uses the same prompt and native image. No image-to-text conversion was used.
Contexts exceeding the 11,000-token branch limit were compacted without changing the images, tools, chosen completions, or rejected completions. All 580 final pairs passed structural, image, prompt-equality, and exact token-length audits.
Chimpanzee-1.1 Training
Chimpanzee-1.1 was trained using multimodal DPO on unquantized Qwen3.6-27B. Training followed a cumulative RPS curriculum:
| Stage | Dataset | Pairs | Learning Rate |
|---|---|---|---|
| Stage 1 | ARC-Witness Level 1 | 75 | 3.333333333333e-6 |
| Stage 1C | ARC-Interactive Level 1 | 75 | 3.333333333333e-6 |
| Stage 2 | ARC-Witness later levels | 144 | 5e-7 |
| Stage 2C | ARC-Interactive Levels 2+ | 286 | 5e-7 |
Stage 2 and Stage 2C use 15% of the high-stage learning rate, implementing the lower-plasticity portion of the RPS curriculum. Each stage initializes from the cumulative adapter produced by the preceding stage.
Training used one epoch per stage, a constant-with-warmup scheduler, a 5% warmup ratio, DPO beta 0.1, and RPO alpha 1.0. LoRA was applied to all linear LLM modules with rank 64, alpha 128, and dropout 0.05. The vision transformer and vision-language aligner remained frozen.
Training used bfloat16 precision, SDPA, reentrant gradient checkpointing, and DeepSpeed ZeRO-3 across two 96 GB H20 GPUs. The per-device batch size was 1, gradient accumulation was 1, and the effective global batch size was 2. The maximum branch length was 11,000 tokens, and the maximum image size was 262,144 pixels.
The final Stage 2C adapter is cumulative and contains the effects of all four curriculum stages.
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