docs: Add missing arguments to DeepScaler evaluation

[butsugiri]

What does this PR do ?

This PR attempts to fix the document for deepscaler experiments

Currently, necessary arguments are missing, which leads to poor evaluation results

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model_name='step_300-hf' dataset_name='aime_2024'
max_new_tokens=2048 temperature=0.0 top_p=1.0 top_k=-1

metric='pass@1' num_tests_per_prompt=1

score=0.0333 (1.0/30)
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By specifying cot.txt (as is done in training setup), the result improves a bit.

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model_name='step_300-hf' dataset_name='aime_2024'
max_new_tokens=2048 temperature=0.0 top_p=1.0 top_k=-1

metric='pass@1' num_tests_per_prompt=1

score=0.1333 (4.0/30)
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By allowing the generation of more than 2048 tokens, the result is even better (This PR)

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model_name='step_300-hf' dataset_name='aime_2024'
max_new_tokens=8192 temperature=0.0 top_p=1.0 top_k=-1

metric='pass@1' num_tests_per_prompt=1

score=0.3667 (11.0/30)
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Issues

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Usage

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[SahilJain314]

Thanks for the PR! Slipped past us for a bit.

[abukharin-nv]

LGTM! I would also suggest increasing max_len to 32K, but that is kind of a subjective choice.

[butsugiri]

@abukharin-nv
Thank you for reviewing my PR! I have set max_len to 32768, and it gave me the following results:

--> I am updating my PR.

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model_name='step_300-hf' dataset_name='aime_2024'
max_new_tokens=32768 temperature=0.0 top_p=1.0 top_k=-1

metric='pass@1' num_tests_per_prompt=1

score=0.3667 (11.0/30)
============================================================

Please let me know if there's anything I can improve.