arXiv (OAI Expanded)
open access
When2Think: Learning When and How Much to Reason
Large Reasoning Models (LRMs) often overthink easy problems and underthink hard ones, leading to inefficient computation allocation. Existing methods regulate generated computation or select between direct answering and explicit reasoning, but do not jointly control whether}to reason and how much co
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
- Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models#1034823
- OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning#1011605
- Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking#1342976
- What is the Role of Small Models in the LLM Era: A Survey#1049587
- First Token Matters: Understanding Safety Collapse in Large Reasoning Models#1012653
Record · ID 1306357
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