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rLLM v0.2: RL Training over General Agentic Programs (Blog Post)

We are excited to release rLLM v0.2, a major upgrade of our RL training framework. In v0.1, rLLM provided agent and OpenAI Gym-like environment abstractions to support training ReACT-style agents. In v0.2, we additionally introduce AgentWorkflowEngine and AgentWorkflowTrainer—more general abstractions that enable arbitrary agentic programs to be trained. Agent builders and researchers can now define multi-agent systems, complex workflows (e.g., solver-judge, planner executor, MCTS), and agentic programs with custom reward functions, and train them with reinforcement learning without rewriting their production code.

Key Features in v0.2

  1. Support the official verl==0.5.0 as training backend, no custom verl fork anymore! verl==0.5.0 comes with support of the following features which are now supported in rLLM (@kylemontgomery1):
    • Megatron training support (@jeewoo-lee)
    • SGLang as the rollout engine, in addition to vLLM.
  2. Introduce AgentWorkflowEngine, which enables passing in arbitrary agentic programs for training. (@kylemontgomery1)
  3. Support more agents and environments
    • Terminus and TerminalBench (@JasonWei05)
    • Tongyi DeepResearch agent (@yayashuxue)
    • AppWorld and AppWorldReactAgent (@sunan135)
  4. Integration with other agentic framework/SDK
    • Strands SDK from AWS
    • SmolAgents

What's Changed

New Contributors

Full Changelog: https://github.com/rllm-org/rllm/commits/v0.2.0

Source: README.md, updated 2025-10-16