Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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1.7 KiB
Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback (RLHF) is a technique that fine-tunes language models using human-generated preference data to align model outputs with desired behaviors. vLLM can be used to generate the completions for RLHF.
The following open-source RL libraries use vLLM for fast rollouts (sorted alphabetically and non-exhaustive):
See the following basic examples to get started if you don't want to use an existing library:
- Training and inference processes are located on separate GPUs (inspired by OpenRLHF)
- Training and inference processes are colocated on the same GPUs using Ray
- Utilities for performing RLHF with vLLM
See the following notebooks showing how to use vLLM for GRPO: