This week saw a renewed wave of interest in reinforcement learning (RL) for AI agents, especially with GRPO. So we thought it would be useful to bring together the most relevant open-source tools that anyone can use to build, train, and optimize their own agents.
TL;DR: Agent RL training frameworks help improve AI agents through trajectories, rewards, tool use, and environment interaction. Some focus on GRPO and RLHF, others on scalable rollouts, multi-turn agents, long-horizon tasks, or multi-agent workflows. The choice depends on your agent stack. →
Agent RL Training Frameworks Compared
Framework | Best for | Main strength |
|---|---|---|
OpenPipe ART | Agent-first GRPO training | Ergonomic RL loop for multi-step agents |
verl-agent | Scalable long-horizon agents | Built on veRL with PPO, GRPO, DAPO, RLOO, REINFORCE++ |
Agent Lightning | Existing agent stacks | Adds RL without rewriting the agent |
Unsloth | Local fine-tuning and GRPO | Consumer-GPU-friendly training and export |
OpenRLHF | Distributed RLHF and agent RL | Ray, vLLM, DeepSpeed, PPO, GRPO, RLOO |
SkyRL | End-to-end agent RL stack | Training, inference, environments, and evaluation |
NVIDIA Polar | Rollout orchestration | Makes existing agent harnesses RL-ready |
Agent-R1 | Step-level MDP agent training | Explicit observation-action-reward transitions |
RAGEN | Trajectory-level agent RL | Diagnostics for reward quality and reasoning collapse |
Marti | Multi-agent RL workflows | Debate, chain-of-agents, mixture-of-agents |
1.OpenPipe ART (Agent Reinforcement Trainer)
An agent reinforcement trainer that trains multi-step agents through experience and environment interaction via GRPO. Your app defines the task and reward, and ART handles the RL loop: inference, trajectory scoring, GRPO optimization, checkpointing and LoRA updates.
When to use: A good option if you want an ergonomic agent-first GRPO harness rather than a generic RLHF stack. It's useful for multi-step tasks like tool use, email search, MCP, games and reasoning workflows
2. verl-agent
An agent RL framework that is an extension of ByteDance’s veRL (Volcano Engine RL) – RL training library for post-training LLMs that supports PPO, GRPO, DAPO, RLOO and REINFORCE++ algorithms. But verl-agent is made for training multi-step LLM agents. It uses step-wise agent-environment interaction with customizable memory and per-step inputs.
When to use: Choose this framework if you're training agents that take many actions like web browsing, tool use, search, GUI automation, or embodied tasks and when you need highly scalable long-horizon training and optimization.
3. Agent Lightning
Trains AI agents via RL without rewriting the agent itself. This tool from Microsoft works with popular agent frameworks such as LangChain, OpenAI Agents SDK, AutoGen, CrewAI, and Microsoft Agent Framework, collecting trajectories and optimizing prompts or policies through RL, SFT, and other methods.
When to use: Good if you want to improve an existing agent with RL without rebuilding your stack. Works for both single-agent and multi-agent systems.
4. Unsloth
A local UI for running, fine-tuning, and RL-training LLMs, VLMs, audio, and embedding models. It combines inference, dataset creation, fine-tuning, GRPO-based reinforcement learning, model export, and monitoring in a single interface while reducing training memory requirements through custom kernels and optimizations.
Unsloth is primarily a model training toolkit, but it can train agent models using RL algorithms such as GRPO and supports tool calling, long-context RL, and multimodal agents.
When to use: It's especially useful for LoRA fine-tuning, GRPO/RL training, dataset preparation, and experimenting with models on consumer GPUs or Apple Silicon. A good choice if you want an easy way to run and train open models
5. OpenRLHF
A high-performance RLHF (Reinforcement Learning from Human Feedback) framework built around Ray, vLLM, and DeepSpeed. It supports PPO, GRPO, REINFORCE++, RLOO, reward modeling, and both single-turn and multi-turn agent training through a unified agent-based execution pipeline.
When to use: Good if you need a scalable RLHF or agent-training stack for large models. It's particularly useful for distributed RL training, custom reward functions, multi-turn agents, and production-scale workloads using Ray and vLLM.
6. SkyRL
A modular full-stack RL framework for LLMs that combines training, inference, agent training, and RL environments in a single ecosystem. It includes components for RL training (SkyRL), long-horizon agent optimization (SkyRL-Agent), and Gymnasium-based environments (SkyRL-Gym).
When to use: Good if you want an end-to-end stack for training and evaluating tool-using especially good for long-horizon agent RL, multi-turn workflows, SWE-Bench-style tasks, and building custom environments with Gymnasium.
7. NVIDIA’s Polar
This one is technically a rollout system, but it’s interesting in this list because it turns existing agent harnesses into RL-ready environments without code changes. It provides rollout orchestration, trajectory construction, evaluation, and scalable distributed execution through a server-based architecture.
When to use: Polar is useful if you already have an agent system and need scalable rollouts for RL training, especially for multi-step tasks and integration with trainers like Slime, verl, or NeMoRL.
8. Agent-R1
Trains multi-step LLM agents, treating each agent action as a step-level MDP (Markov Decision Process) transition. It explicitly models observations, actions, tool feedback, rewards, and environment state rather than optimizing a single growing prompt.
When to use: Good if you're training tool-using or environment-interacting agents with multi-step reasoning.
9. RAGEN
Built around the StarPO algorithm, it optimizes full reasoning-and-action trajectories. It includes built-in environments (WebShop, SearchQA, DeepCoder, Lean, Sudoku, Sokoban) and diagnostics for analyzing agent RL failure modes like reasoning collapse and poor reward quality.
When to use: Use it when you want to understand why RL training succeeds or fails and when you need multi-turn agent training and trajectory-level optimization.
10. Marti
A multi-agent RL training framework which supports graph-based agent workflows – debate, chain-of-agents, and mixture-of-agents, combining centralized coordination with distributed policy training across multiple agents.
When to use: Good for tree-search-based RL, agent reasoning, code generation, debate-style workflows, and heterogeneous agent teams.
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Don’t forget to check out our in-depth guides on reinforcement learning and GRPO to pick up with the basics and advanced approaches. You may find it helpful!
FAQ
What is an agent RL training framework?
An agent RL training framework is a tool for improving AI agents with reinforcement learning. It usually collects trajectories, scores actions or outcomes with rewards, and updates the model, policy, prompt, LoRA adapter, or agent behavior based on task performance.
When should you use agent RL instead of supervised fine-tuning?
Use agent RL when the task depends on multi-step behavior, tool calls, environment feedback, or final outcomes that are easier to reward than imitate. Supervised fine-tuning is better when you already have high-quality examples of the exact behavior you want.
What is the difference between RLHF and agent RL?
RLHF usually aligns model responses with human preferences, often in single-turn or dialogue settings. Agent RL trains behavior across multi-step trajectories, where the model may call tools, observe results, update memory, retry actions, and optimize for task success over time.
Which agent RL framework should you choose?
Choose ART or Unsloth for accessible GRPO-style experimentation, verl-agent or OpenRLHF for scalable training, Agent Lightning if you already use an agent framework, Polar for rollouts, RAGEN for diagnostics, SkyRL for full-stack environments, and Marti for multi-agent workflows.
Why do agent RL frameworks matter?
They matter because agents are no longer just chatbots. They browse, code, search, use tools, control environments, and collaborate with other agents. Training these systems requires feedback from complete trajectories, not only isolated prompt-response pairs.
