suggested fixes and improvements to rl/train.py

[josancamon19]

1. Resume, checkpointing, and logging are separate mechanisms; W&B doesn’t have a resume option.
2. Expose loss_fn configuration parameters.
3. It’d be great to offer more optionality around the 2025 recipes, DAPO, GFPO, GSPO, etc. Why? This is an assumption, but I think a lot of people import the cookbook as a dependency and reuse many of the abstractions/configurations you’ve created (what I've been doing the past few weeks), so it would make sense (imo) to make it as broadly useful as possible.

[josancamon19]

@joschu, I can create a PR for 1,2 (and 3) if that would be useful, specially 1,2 should be easy, then can provide a more detailed issue on 3 before starting it, lmk

[tyler-griggs]

Thanks @josancamon19!

• A PR for adding the W&B resume option for (1) would be great
• I believe (2) was resolved in Add loss_fn_config #156
• It would be helpful to provide popular recipes out of the box, so a more detailed issue on this would be welcomed

[josancamon19]

#305 @tyler-griggs

[josancamon19]

#156 yes! it was solved there, maybe considering to update all recipes to reflect this, not only distillation ones

[josancamon19]

Regarding training options (third point)

The key idea is that verl and other frameworks already include default implementations of many algorithms, so it’d be great for Tinker to provide a standard interface for some of them, especially since the key components are already there.

1. For PPO, we can already set the clipping range via loss_fn_config, but having a clipping=default|higher|custom abstraction would be a nice improvement (this depends a lot on the ICP).
2. A preconfigured option for dynamic sampling, as in DAPO.
3. Clipping at the token level vs. the sequence level, as in GSPO.

Overall, the goal is to make experimentation easier and provide off the shelf recipes that people can reuse, as well as mix and match components to explore variants. This would make the out of the box experience more versatile than existing framework implementations.

• The simplest step would be to expose clipping options by default default | clip_higher abstracted away from the loss config.
• The more interesting step would be to add a PPOOptions (or similar) that supports default | DAPO out of the box, ensure rl/train.py handles it by default, and include a recipe comparing both methods.