New features and optimizations
- Added public-facing official Dockerfile for NeMo-Aligner.
- PPO: memory optimization to help avoid OOM in the actor when sending training data to the critic.
- PPO: it is now possible to use a custom end string in `sampling_params.end_strings` that is different from `<extra_id_1>`.
- SFT: added support for custom validation metrics based on model generations.
- Added the ability to do multi-epoch (cfg.max_epochs > 1) training for reward models, DPO, PPO, and SFT
- Added the SPIN (Self-Play Fine Tuning) algorithm (https://arxiv.org/abs/2401.01335) which allows SPIN SFT training using SFT-format dataset files
- SFT/SteerLM: added LoRA tuning as an option besides full fine-tuning, only attention_qkv layer is supported
Breaking changes
- We have changed the shuffle logic in the data sampler to support multi-epoch training, so training runs using identical parameters
will not give the same results anymore because the shuffle logic has changed (specifically the seed value is modified slightly per epoch).
If you run CI/regression type tests, then be warned that the test may break due to this shuffle change.
Bug Fixes
- Fixed a potential issue when the base model's `model.data.data_prefix` config is a list and is about to be overridden with
a dictionary from the training configuration.
- `exp_manager.max_time_per_run` is now respected, the trainers will save and run validation before exiting if we've reached the time limit.
- Fixed crash in PPO when using a separate reward model server (i.e., with `combine_rm_and_critic_server=False`).
- Fixed crash when LR scheduler is not specified