verlByteDance's RL post-training library (HybridFlow): PPO/GRPO dataflows in a few lines, FSDP/Megatron training with vLLM/SGLang rollouts, production-proven at frontier scale.
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 → 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 switchBoth are research RL frameworks behind frontier-scale releases. slime is THUDM's Megatron + SGLang stack from the GLM line; Molt is NVIDIA's PyTorch-native, Ray + vLLM stack with a Gymnasium-shaped agent contract.
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 are newer fully-async RL frameworks; Molt uses Ray + vLLM + FSDP2, Meshy SGLang + torchtitan over TransferQueue.
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 switchTRL is the general post-training library on Transformers — SFT, DPO, GRPO on a single node up. Molt is purpose-built for fully-async multi-turn agentic RL at MoE scale; reach for TRL until your environment is multi-step and your model won't fit.
Full comparison →