rllm
Active·★ 5.6k·Apache-2.0·Updated 2026-05-28
★ Trending
Democratizing Reinforcement Learning for LLMs
rLLM is an open-source framework designed for post-training language agents using reinforcement learning. It allows users to easily build, train, and deploy custom agents and environments for real-world workloads.
#Reinforcement Learning#Language Agents#LLM#Deep Learning Framework#Post-training
01
Features
01Open-source framework for reinforcement learning-based post-training of language agents.
02Supports building, training, and deploying custom agents and environments.
03Offers multiple training backends including 'verl' and 'tinker'.
04Enables LoRA and VLM training for advanced models.
05Includes AgentWorkflowEngine for training over arbitrary agentic programs.
02
Compatibility
Python
Supported
Verified via docs
verl
Native Backend
Verified via docs
tinker
Native Backend
Verified via docs
uv
Recommended
Verified via docs
Docker
Supported
Verified via docs
03
Quick start
1
$ uv pip install "rllm[verl] @ git+https://github.com/rllm-org/rllm.git"
04
Use cases
↳Training powerful coding models for tasks like code generation and bug fixing.
↳Developing sophisticated software engineering agents for automated tasks.
↳Building and evaluating multi-agent systems using reinforcement learning techniques.
05
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Comments
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- OOaklyn ClarkMay 2, 2026
Multi-agent democratizing coordination is handled better than competing frameworks. Solid addition to the AI tooling stack.
- RRiley JohnsonApr 24, 2026
Multi-agent democratizing coordination is handled better than competing frameworks — democratizing reinforcement learning for llms. Integrates well with existing democratizing s...
- AAlex MartinezApr 21, 2026
Multi-agent democratizing coordination is handled better than competing frameworks — democratizing reinforcement learning for llms. Good documentation, reduces onboarding time.