How Deep Can LLMs Learn to Reason? Expressiveness Is Key
cs.AI, cs.CL
Submitted: 2026-05-07
Updated: 2026-09-26
Code: https://github.com/wtl666wtl/ScaleLogic
Terminology
Sources
- Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- ReSyn: Autonomously Scaling Synthetic Environments for Reasoning Models
- OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
- Measuring Mathematical Problem Solving With the MATH Dataset
- Scaling Laws for Autoregressive Generative Modeling
- Training Compute-Optimal Large Language Models
- R-Zero: Self-Evolving Reasoning LLM from Zero Data
- GPT-4o System Card
- OpenAI o1 System Card
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- Scaling Laws for Neural Language Models
- The Art of Scaling Reinforcement Learning Compute for LLMs
- Solving Quantitative Reasoning Problems with Language Models
- DeepSeek-V3 Technical Report
- SATURN: SAT-based Reinforcement Learning to Unleash LLMs Reasoning
- SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
- R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
- ReFT: Reasoning with Reinforced Fine-Tuning
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