How do you efficiently train RLHF at scale?
hardAnswer
- (1) Sample generations offline, cache.
- (2) Reward model inference batched separately from policy.
- (3) Use PagedAttention / vLLM for fast rollout inference.
- (4) DeepSpeed-Chat / trlX / OpenRLHF frameworks handle mixed policy-value training.
- (5) Reference model on CPU or shared across GPUs.
- (6) Reward normalization + advantage whitening per-batch.
- (7) Save intermediate checkpoints — training is fragile.
- Modern practice: careful engineering matters more than algorithm.
Check yourself — multiple choice
- Random
- Cache generations + batch reward inference + PagedAttention rollouts (vLLM) + frameworks (DeepSpeed-Chat / trlX / OpenRLHF) + ref model on CPU/shared + normalize + checkpoints
- Just PyTorch
- Not real
RLHF at scale: cache + batch + vLLM + frameworks + engineering care.
#llm#engineering
Practise Reinforcement Learning
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