Agent Lightning v1.0: Harnessed Agentic RL for Training Agents
Agent Lightning v1.0, detailed in arXiv preprint 2608.17528, introduces an innovative reinforcement learning (RL) method by incorporating the deploy-time harness into the model's post-training phase. This approach stems from the Agent Lightning framework, which distinguishes the training engine from the environment interaction loop through an LLM endpoint proxy. Consequently, various agents can engage in RL training without the need to modify the harness, which is utilized by platforms such as verl, Uni-Agent, AReaL 2.0, slime, and Polar. The authors refer to this concept as 'harnessed agentic RL,' where the harness governs the interaction loop, presenting challenges such as retokenization and loss normalization. The framework is designed to provide a flexible and scalable training solution for developers refining intricate agent systems.
Key facts
- Agent Lightning v1.0 is detailed in arXiv preprint 2608.17528.
- The original Agent Lightning introduced a disaggregated architecture with an LLM endpoint proxy.
- The architecture allows arbitrary agents to be connected to RL training.
- Frameworks adopting this approach include verl, Uni-Agent, AReaL 2.0, slime, and Polar.
- The paradigm is called harnessed agentic RL.
- In this paradigm, the harness owns the environment interaction loop while the trainer sees only LLM request-response pairs.
- Challenges include retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling.
- These challenges can substantially affect training stability and effectiveness.
- The harness manages tools, context, and control flow in modern agent systems.
Entities
Institutions
- Agent Lightning
- arXiv
- verl
- Uni-Agent
- AReaL 2.0
- slime
- Polar