Most open RLHF stacks choke past a few billion parameters because actor, critic, reward, and reference models fight over the same GPUs. OpenRLHF's bet is scheduling, not just kernels: it uses Ray to place each model on its own GPU group, hands rollout generation to vLLM, and runs the rest on DeepSpeed ZeRO-3 — which is why 70B+ RLHF runs become routine instead of heroic.
What Sets It Apart
- Disaggregated placement means you can right-size GPUs per role (a big actor, a small reward model) instead of co-locating everything and wasting memory.
- vLLM-driven sampling removes the usual RLHF bottleneck, where slow generation, not gradient steps, dominates wall-clock time.
- One algorithm-agnostic loop covers PPO, GRPO, RLOO, REINFORCE++/baseline, and DAPO, alongside SFT, reward-model training, and DPO/IPO — so switching methods is a flag, not a rewrite.
- Async and agentic multi-turn modes via a token-in-token-out interface let it train reasoning and tool-use policies, not just single-turn preference tuning.
Who It's For
Great fit if you're running serious RLHF or RL-for-reasoning experiments on multi-node clusters and want vLLM throughput without gluing the pieces together yourself. Look elsewhere if you have a single GPU or just need quick DPO/SFT on small models — the Ray-plus-vLLM-plus-DeepSpeed setup is real operational overhead that only pays off at scale.
