Samsung Proposes PROGRESS: Coverage-Guided RL to Train Search-Augmented LLM Agents
_reachsumit · x · 2026-08-04
Samsung's research team proposes PROGRESS, a coverage-guided Reinforcement Learning (RL) framework designed to overcome the limitations of outcome-only rewards in search-augmented LLM agents.
- Core Mechanism: Integrated into an R1-style training framework, it uses a frozen teacher model to decompose complex queries into essential sub-queries. These serve as coverage rewards to guide the policy model's search behavior without dense process-level supervision.
- Results: Experiments show that explicitly supervising query decomposition significantly improves overall task performance, highlighting the importance of shaping search strategies in agentic LLMs.
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