From Outcomes to Strategies: Learning Strategy Utility for Mathematical Reasoning
cs.AI
Submitted: 2026-09-26
Updated: 2026-09-26
Terminology
Sources
- Graph of Thoughts: Solving Elaborate Problems with Large Language Models
- Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries
- Training Verifiers to Solve Math Word Problems
- Process Reinforcement through Implicit Rewards
- Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning
- On Designing Effective RL Reward at Training Time for LLM Reasoning
- OpenThoughts: Data Recipes for Reasoning Models
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
- DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
- Measuring Mathematical Problem Solving With the MATH Dataset
- LoRA: Low-Rank Adaptation of Large Language Models
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks
- LLM Reasoning with Process Rewards for Outcome-Guided Steps
- Proximal Policy Optimization Algorithms
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Solving math word problems with process- and outcome-based feedback
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Masked-and-Reordered Self-Supervision for Reinforcement Learning from Verifiable Rewards
- Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
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