arXiv (All)
open access
One Policy, Any Budget: Internalizing Budget-Aware Search via Reinforcement Learning
While reinforcement learning has enabled LLM-based search agents to invoke external tools, existing methods train under fixed budgets and cannot adapt when constraints vary at deployment. We propose AnySearch, a framework that enables a single policy to perform budget-aware search under any budget c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning#1130653
- Goal-Persistent Coding Agents as Scientific Performance Engineers: A Fixed-Radius Nearest-Neighbor Case Study#1115936
- TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling#444606
- Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents#1141405
- An Imperfect Verifier is Good Enough: Learning with Noisy Rewards#1117363
Record · ID 820821
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive