Which hyper-parameters in the standard VERL configuration do not take effect?
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start with the VERL configuration documentation and the referenced trainer.py lines 463-469. Trace the listed parameters—rollout.val_kwargs, data.truncation, and rollout.top_p—to determine which are effective when passed to agl.VERL. Done means documenting a clear whitelist or blacklist of supported configurations.
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Description
This is the full VERL config, https://verl.readthedocs.io/en/latest/examples/config.html
I noticed that some parameters seem not work when passed to agl.VERL. For example:
- actor_rollout_ref.rollout.val_kwargs
- data.truncation
- actor_rollout_ref.rollout.top_p (I'm not sure if it takes effect, as it is not explicitly set like actor_rollout_ref.rollout.temperature)
Could you provide a blacklist/whitelist to clarify which configs are effective or not?
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- Python
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