slimeTHUDM's RL post-training framework behind the GLM releases — Megatron training plus SGLang rollouts with native arg pass-through, and pluggable reward, verifier and agentic data-generation workflows.
Why switchSame job — large-scale RL post-training. verl bets on multi-backend flexibility (FSDP/Megatron × vLLM/SGLang) where slime hard-commits to Megatron+SGLang; pick verl when your infra is vLLM- or FSDP-shaped.
Full comparison → trlHugging Face's post-training library: SFT, DPO, GRPO, KTO and reward-model trainers on top of Transformers — from a Colab LoRA run to multi-GPU deployments.
Why switchSame goal — a post-trained model — at opposite ends of the infra spectrum: TRL for Hub-native single-node SFT/DPO/GRPO, verl when the job needs disaggregated rollout engines and multi-node RL dataflows.
Full comparison → labs-moltMolt: NVIDIA's agentic-first RL framework in ~9K lines — Ray for placement, vLLM for rollout, AutoModel + FSDP2 for training — fully async, multimodal, multi-turn, scaling to 1T-class MoE.
Why switchBoth are open RL post-training frameworks with vLLM rollouts and FSDP/Megatron training. verl (HybridFlow) optimises for breadth of algorithms and dataflows; Molt optimises for an agentic-first, single-actor design you can read in an afternoon.
Full comparison → MeshyOpenBMB's RL training framework where every role — inference, training, rollout — is an independent service talking over one TransferQueue data plane. Built on SGLang and torchtitan.
Why switchBoth run GRPO-style RL post-training with separate rollout and training engines; verl drives HybridFlow from a controller, Meshy drops the driver and coordinates services through queue readiness.
Full comparison →