Dynamic LLM/RLHF Trainers: config-driven framework support in the SDK
- Dominant language
- Python
- Stars
- 148
- Forks
- 262
- Avg merge
- 1d 2h
- Merged PRs (30d)
- 1
Description
Narrowed slice of #285 (canonical issue), per the 2026-07-15 community call: **config-driven LLM/RLHF trainers only** — a `ConfigTrainer` carrying a `FrameworkConfig` (framework label + entrypoint command + argument rendering), explicit decorator registration, and TRL (SFT/DPO/GRPO) as the first framework. `BuiltinTrainer` keeps its exact signature; nothing is deprecated.
**Out of scope** (remain under #285): function-driven trainers (`TorchTrainer`, `DeepSpeedTrainer`, ...), `RuntimeConfig`, control-plane extension points.
**Related:** #308 (foundation proposal by @szaher) · kubeflow/trainer#2839 (prior art) · kubeflow/trainer#3508 / kubeflow/trainer#3718 (in-flight GRPO via TRL, for which this provides the SDK surface)
- [ ] KEP PR
- [ ] Phase-1 implementation
- [ ] PoC: TRL path end-to-end on kind
cc @tariq-hasan
Contributor guide
Research direction
Start by reading canonical issue #285, foundation proposal #308, and the cited kubeflow/trainer PRs for existing GRPO and TRL context. Done means producing the KEP PR, completing the phase-1 implementation, and demonstrating the TRL path end to end on kind.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- kubernetes, python
- Domain
- ai, backend-api-design, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100