rLLM

rLLM is a framework for training AI agents with reinforcement learning, allowing integration into any existing agent framework with minimal code changes.

rLLM is a framework designed to train AI agents using reinforcement learning. The system workflow engine executes multiple parallel agent instances to collect rollouts, while a LiteLLM proxy routes requests and captures token IDs and logprobs. A transformation pipeline then groups trajectories for advantage computation. The training backend, supporting verl or tinker, handles the policy update. This tool targets developers looking to apply reinforcement learning to existing agent frameworks, offering battle-tested results on benchmarks like SWEBench-Verified and LiveCodeBench.

rLLM at a glance

Best for: Software engineer, Researcher
Runs on: CLI
Pricing: Free
Released: January 2025
Category: AI coding tool
Website: docs.rllm-project.com

Key features

Compatible with any agent framework
Near-zero code changes required
CLI-first workflow implementation
Support for multiple RL algorithms
Two distinct training backends
Production-ready deployment support

Use it for

Training software engineering agents
Improving financial analysis models
Optimizing coding models for competitive programming benchmarks

Alternatives to rLLM

Agent Lightning — Train existing artificial intelligence agents with reinforcement learning and zero code changes.
ART — Enhances LLM agent performance and reliability using reinforcement learning techniques.
RLinf — Scalable open-source framework supporting real-world robots and simulators.
RubyLLM — It provides a unified API for 17 AI providers with native Ruby and Rails integration.
OpenRLHF — High-performance distributed architecture and unified agent-based execution
NeMo Molt — It scales fully-async agentic RL to frontier MoE scale with a small PyTorch stack.
MLflow — Complete observability and evaluation for production AI apps.
Llmll — The compiler proves each function against its contract with Z3 before merging.
vLLM — Maximizes throughput with PagedAttention and continuous batching.
WebLLM — Runs high-performance language models directly inside web browsers.
AReaL — Bridges foundation model training with modern agent-based applications.
RAGEN — Diagnoses agent failures and stabilizes reasoning training.

All rLLM alternatives