PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training

Researchers introduce PRAISE, a new framework that improves training efficiency for AI agents performing complex search tasks like multi-hop question answering. The method addresses key limitations in current reinforcement learning approaches by reusing partial search trajectories and providing intermediate rewards rather than only final answer feedback.

• →PRAISE framework significantly improves data efficiency in training AI agents for complex search and reasoning tasks.
• →The method solves reward sparsity issues by providing step-level feedback during training rather than only final answer evaluation.
• →A single shared model handles both search policy learning and answer evaluation, eliminating need for separate reward models.
• →Experimental results show consistent performance improvements over existing baselines on multi-hop QA benchmarks.
• →The approach reduces computational waste by reusing expensive long-horizon rollouts during training.