PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Existing search-augmented LLM agents are trained using Reinforcement Learning to boost its reasoning capabilities. However, these approaches primarily rely on outcome-level rewards, which provide little supervision over search behavior and overlook agent's ability to decompose complex queries properly. To mitigate this issue, we propose PROGRESS which utilizes teacher-guided coverage reward to explicitly shape decomposed query generation of the policy model. During training, frozen teacher models are used to decompose complex queries into essential search queries. These essential search queries are utilized to guide the search behavior of the policy model. Integrated into an R1-style trai...
| Authors | Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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