← Wednesday, October 7, 2026AgentsGuide
Multi-turn RL fine-tunes search agents to use tools and decide when to stop
The guide frames fine-tuning as a way to teach smaller models a search environment, avoiding the cost of frontier models and the data burden of expert SFT trajectories. This is relevant to teams optimizing agent capability against latency and cost.
- What
- Multi-turn reinforcement learning fine-tunes search agents to make tool-use decisions across multiple rounds.
- How
- The approach generates training data with multi-turn rollouts, collects trajectories, and optimizes the model with policy-gradient algorithms using final-outcome rewards.
- Why it matters
- It can teach a smaller model environment-specific behavior, aiming for faster and cheaper inference than relying on a frontier model.
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