Does ROLL support DAPO training for LLMs or VLMs?
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- Python
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Description
I noticed that in `ROLL/roll/pipeline/rlvr/rewards/math_rule_reward_worker.py`, there are some parameters related to DAPO, such as `epsilon_high`, `mask_truncated_completions`, and `loss_type`.
However, I couldn’t find any description of these parameters in the official documentation or in `ROLL/docs_roll/docs/#U7b80#U4f53#U4e2d#U6587/#U5feb#U901f#U5f00#U59cb/config_guide_cn.md`.
Does ROLL currently support DAPO? If so, how should I use these parameters?
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Research direction
Start with ROLL/roll/pipeline/rlvr/rewards/math_rule_reward_worker.py and inspect how epsilon_high, mask_truncated_completions, and loss_type are used. Compare those parameters with docs_roll/docs/#U7b80#U4f53#U4e2d#U6587/#U5feb#U901f#U5f00#U59cb/config_guide_cn.md. Document whether DAPO is supported and explain how these parameters should be configured, if their behavior is established.
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Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100