labs-molt vs trl
Molt: 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. — versus — Hugging 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.
TRL 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.
| labs-molt | trl | |
|---|---|---|
| Stars | 1.0k | 19k |
| Forks | 100 | 3.0k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | yesterday | 2 days ago |
| Topics | training | training |
| Curated connections | 5 | 9 |
labs-molt — the curator's take
The RL stack to read end-to-end: the agent is the program (one Gymnasium-aligned Env.step() or ChatAgent.run(), reward is any Python you write), the trainer is a single actor with an optional KL reference, and Ray owns the async queues between rollout, training and weight sync. Token-first contract keeps ids, logprobs, action ranges, rewards and multimodal tensors aligned so multi-turn tool use and VLM environments share one format. Same script trains 8B and DeepSeek-V3-class MoE with expert parallelism. NOT for breadth — it deliberately has fewer algorithms and integrations than verl or TRL — and NOT for SFT-only work; it's for research groups running agentic RL who want every gradient one file away.
trl — the curator's take
The on-ramp for post-training: if your model is on the Hub and your job fits SFT/DPO/GRPO/KTO, a Trainer class gets you a running job in an afternoon — and nothing else scales down to a free Colab as gracefully. PEFT/LoRA, quantized training and accelerate multi-GPU come along for free. NOT for frontier-scale RL dataflows (that's verl/slime territory — no Megatron, no disaggregated rollout engines), and the trainer abstraction that makes it easy also hides the loss mechanics: when results surprise you, read the trainer source before blaming the data.