deepspeedai / deepspeedai/DeepSpeedExamples
Question about `compute_rewards` in DeepSpeedChat
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Description
If I understood correctly, in DeepSpeedChat, the rewards for each token are calculated by adding KL penalties with the same reward_score on each sample (episode), as shown in this link.
However, the library trl and trlx handles this differently, they only add the reward_score to the last token, as shown here.
Are there any reasons why DeepSpeedChat adds reward_score to each token?
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Research direction
Start by reading applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/ppo_trainer.py around the linked compute_rewards implementation. Compare its token-level reward handling with the linked trl and trlx implementations, then check related RLHF or PPO documentation. Done means the rationale for applying reward_score across tokens is documented or the behavior is clarified.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100