Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning
cs.AI
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- The Llama 3 Herd of Models
- Distilling the Knowledge in a Neural Network
- Less is More: Recursive Reasoning with Tiny Networks
- Qwen3 Technical Report
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
- VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection