CPU-ARC caches LLM reasoning traces for cheaper CPU reuse
The paper reports on-par results with frontier models on hard math and science tasks at 16–291x lower cost.
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Stories tagged #math-reasoning
DTCA assigns token-level credit for RL on diffusion language models
On LLaDA-8B-Instruct, authors report gains of up to 9.0 points on MATH500 and 5.3 on GSM8K.
Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
SCALE gates frozen SFT features with entropy-trained controls, improving math and code scores on three Qwen models.
Bellman Policy Optimization
Bellman Policy Optimization, a critic-free RLVR method derived from Policy Mirror Descent, improves LLM mathematical reasoning without intermediate state-value estimation.
Not All Prompts Are Equal: Exploration-Guided Prompt Scaffolding for Multimodal Reinforcement Post-Training
Exploration-guided prompt scaffolding rewrites training prompts by Exploration Potential Score, boosting multimodal RL post-training accuracy up to 11.5%.
Learning to Solve Hard Problems in RL for LLMs by Never Giving Up
Paper introduces Never Give Up adaptive sampling, fixing RL's 'Matthew Effect' where compute is wasted on easy problems and hard problems see little improvement.
ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.
FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience
FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.