Ross ROSS = Recommend OSS · open-source software intelligence for agents

PrimeIntellect-ai/prime-rl

Agentic RL Training at Scale observed · 2026-08-28

github.com/PrimeIntellect-ai/prime-rl · Python · Apache-2.0 (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
PrimeIntellect-ai/prime-rlmain87

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