mathematical-reasoning

Trust Region Policy Distillation (TOP-D) stabilizes on-policy distillation by dynamically constructing a proximal teacher, providing theoretical convergence guarantees and empirical gains in mathematical reasoning tasks with zero additional overhead.

TREK is a staged procedure that uses distillation to expand exploration support for policy optimization, improving performance on mathematical reasoning and agentic tasks beyond standard GRPO.

This paper introduces dOPSD, an on-policy self-distillation method for diffusion language models that leverages internal denoising trajectories to improve mathematical reasoning and code generation.

Introducing Leanstral 1.5, a 119B parameter (6B active) open model for formal proof engineering in Lean 4, achieving 100% on miniF2F, state-of-the-art scores on PutnamBench and FATE benchmarks, and discovering previously unknown bugs in open-source repositories.

SLIM-RL introduces a risk-budgeted random-masking reinforcement learning method for diffusion LLMs that avoids trajectory slicing, achieving state-of-the-art results on math and code benchmarks with significantly fewer training samples.

A study finds that reinforcement learning gains in transformers are concentrated in a single middle layer, and training just that layer can match or surpass full-parameter RL training across multiple models and tasks.

ISM introduces a self-evolving memory system that stores and refines strategy schemas to improve mathematical reasoning for frozen LLMs under continual learning, outperforming baselines on MATH-Hard and OlympiadBench.

This paper investigates knowledge distillation from the DeepSeek-R1 reasoning model to a compact Qwen2.5-7B student model using a Chain-of-Thought corpus built from John O'Bryan Mathematics Competition problems. The fine-tuned student achieves a 4.76 percentage-point improvement on the competition dataset and generalizes to MATH-500, while also analyzing the impact of response length on reasoning quality.

Introduces MetaFlow, a method that trains large language models to generate zero-shot workflows for tasks by combining supervised fine-tuning and reinforcement learning with execution feedback, achieving strong generalization to untrained tasks and operator sets.

PHF proposes a method to distill hidden state trajectories from a privileged teacher to a student during on-policy self-distillation, improving reasoning performance on language models.

PASS is a middleware that fixes three pathologies in process-supervised RL for LLM reasoners, improving GRPO by independently standardizing streams, chunking by value, and using average value density. It shows consistent gains in math reasoning and multi-hop QA.

This paper proposes a self-supervised theorem-discovery algorithm that starts from axioms and inference rules alone, building a theorem library without human priors. Experiments show the discovered theorems are meaningful and improve LLM proof performance.

Riazi-8B is an Urdu large language model fine-tuned for mathematical reasoning, achieving improved performance on MGSM-Urdu through continued pre-training and supervised fine-tuning on Urdu Chain-of-Thought data.

Introduces Strategy-Guided Policy Optimization (SGPO) for LLM reasoning, which replaces trajectory imitation with strategy distillation, improving generalization on math benchmarks.

The article argues that autoregressive language models cannot achieve true understanding of formal mathematics and need verification methods, citing systems like Aleph that rely on strict mathematical proof.

ReNIO enhances on-policy distillation for LLMs by reweighting negative trajectories based on token-level probability ratios, improving reasoning performance in mathematical and code generation tasks.

VeriEvol is a novel framework for scaling reinforcement learning in visual mathematical reasoning by ensuring reliable reward labels through a two-axis approach separating prompt difficulty from answer reliability, using evolutionary operators and hypothesis-testing verification. It achieves significant accuracy gains on a five-benchmark visual-math suite.

This paper presents a cross-lingual mechanistic analysis of mathematical reasoning in LLMs, finding partial overlap of math-associated parameters across languages, concentrated in intermediate layers. English has the largest set of math-relevant parameters, while lower-resource languages have smaller sets.

This paper introduces MathVis-Fine, a framework for fine-grained visual dependency modeling in multimodal mathematical reasoning, along with a new dataset and a two-stage progressive training paradigm that balances answer correctness and visual grounding rewards based on each sample's intrinsic visual dependency level.

This paper proposes E³RL, a reinforcement learning method that uses dynamic epistemic entropy thresholds to enable LLMs to excise local logical defects during generation, overcoming the autoregressive curse in long-horizon reasoning and achieving state-of-the-art results on mathematical reasoning benchmarks like AIME.