Implement a reasoning LLM in PyTorch from scratch, step by step
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Updated
May 25, 2026 - Jupyter Notebook
Implement a reasoning LLM in PyTorch from scratch, step by step
(ArXiv25) Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning
Solving Inequality Proofs with Large Language Models.
An official implementation of "SPARK: Synergistic Policy And Reward Co-Evolving Framework"
ArmLLM 2025 solutions covering ViT from scratch, SigLIP–Qwen LaTeX OCR, GRPO reasoning post-training, inference-time reasoning strategies, and adversarial vision attacks.
AI Benchmark 知识库 — 全面收录各大 AI 公司用来测试模型性能的 Benchmark 题库完整集合
A minimal JEPA-based language model demonstrating latent-space reasoning on GSM8K using a single decoder-only Transformer.
STaR × S1 math pipeline on Qwen2.5-1.5B. LoRA, strict Final: format, ~20–30% acc (OpenR1-Math split).
Data cleaning and structuring pipeline for math reasoning tasks using Qwen3-0.6B for LLM post-training.
A controlled LoRA finetuning study on process supervision for mathematical reasoning with Qwen2.5-Math-7B-Instruct.
수학·출력제약을 하나의 단순 결정공간으로 묶기 — 휴면 · Unified late-stage repair across math + output-constraint domains (Qwen / Mistral 7B / 14B). Cross-domain unification holds; "single universal rule" narrowed
GRPO (Group Relative Policy Optimization) implemented from scratch in PyTorch. 10 ablation experiments.
Comprehensive framework for mathematical reasoning research with dual research capabilities
Small-scale Implementation and Extension of “The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning” (NeurIPS '25)
NDA-safe excerpts of math & economics modeling tasks for LLM reasoning evaluation and numerical verification.
Transforming weak prompts into reasoning machines using Textual Gradients and AdalFlow. Runs on Colab.
GRPO reinforcement learning with verifiable rewards for sub-2B models
Tool-Integrated Reasoning for competition math — weighted voting, difficulty-aware allocation
Evaluating whether prompting strategies (CoT, Self-Consistency) can recover LLM accuracy degradation caused by irrelevant context in GSM-Symbolic math problems
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