arXiv (All)
open access
Training-Free Prompt Selection for Efficient Reinforcement Learning With Partial Rollouts
Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for training reasoning models, but its computational cost makes efficient data selection increasingly important. Since training examples contribute unequally to learning, selecting a small, informative subset 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
- Understanding Enrichment in Reinforcement Learning#1187160
- CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards#975238
- Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR#985983
- Process Rewards for Outcome-Guided Reasoning Steps in LLM Reinforcement Learning#1342440
- V-FiLLM: Verified Financial LLM Reasoning Benchmark#444627
Record · ID 1388795
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