arXiv (All)
open access
PaceReasoner: Label-Free Learning to Answer Briefly from Long Reasoning
Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers with shorter reasoning. We introduce PaceReasoner, a label-free test-time reinforcement learning framework that uses a model's LONG responses to improve
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
- Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models#343514
- Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners#1344048
- Using reinforcement learning to explain psychodynamics#655090
- Using reinforcement learning to explain psychodynamics#654096
- Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature#1123324
Record · ID 1343099
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