PrimeIntellect-ai/prime-rl
Agentic RL Training at Scale observed · 2026-08-28
Health v2 · maintenance only
87/100
- Activity 99
- Release rhythm 99
- Longevity 40
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 22.5
- age_days: 561
- days_rel: 8
- days_push: 7
- n_releases_24m: 9
Adoption not part of the score
1975 stars · 416 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
prime-rl is a Python framework for large-scale, fully asynchronous reinforcement learning training of language models, built on FSDP2 for training and vLLM for inference. It supports scaling to 1000+ GPUs, integrates with the Prime Intellect Environments Hub for agentic RL environments, and covers end-to-end post-training including SFT, RL, and evals.
Use cases
- train LLMs with reinforcement learning at scale
- run agentic RL post-training on large MoE models
- fine-tune language models with SFT and RL pipelines
- deploy multi-node RL training jobs on Slurm or Kubernetes
- evaluate and post-train models on agentic environments like SWE
- train vision-language models with RL
When to choose
- you need to scale RL training to hundreds or thousands of GPUs
- you want asynchronous, high-throughput agentic RL training
- you need integrated SFT, RL, and evaluation in one framework
- you want native integration with verifiers environments and the Environments Hub
- you need optimized support for large MoE models with expert and context parallelism
When to avoid
- you only need simple single-GPU fine-tuning with minimal setup
- you need a lightweight RL library for small models or quick experiments
- you are not working with language models or agentic RL tasks
- you lack access to multi-GPU or multi-node infrastructure
Facets
framework · maturity active
llm-training reinforcement-learning machine-learning gpu-computing benchmarking reinforcement-learning large-language-models machine-learning gpu-computing artificial-intelligence python cloud reinforcement-learning rlhf post-training sft vllm fsdp2 slurm agentic-training moe distributed-training verifiers environments-hub multimodal fp8 evals gpu linux docker kubernetes
1 source
- readme: https://github.com/PrimeIntellect-ai/prime-rl · fetched 2026-08-28 · 71f2fe33a853
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| PrimeIntellect-ai/prime-rl | main | 87 |
For agents
markdown · JSON · MCP: product_card(name="PrimeIntellect-ai/prime-rl")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem