https://github.com/j-bagel/treecut-math
공식 코드TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM Hallucination Evaluation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jb-01/lora-tle
공식 코드Token-Level Adaptation of LoRA Adapters for Downstream Task Generalization
https://github.com/jerry-chee/discquant
공식 코드DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory
https://github.com/jiangjin1999/LogicPro
공식 코드LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
https://github.com/jikai0wang/speculative_cot
공식 코드Efficient Reasoning for LLMs through Speculative Chain-of-Thought
https://github.com/jinpz/dtv
공식 코드Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
https://github.com/jl-er/bone
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jl-er/disha
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/joeying1019/adactrl
공식 코드AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
https://github.com/joyheyueya/declarative-math-word-problem
공식 코드Solving Math Word Problems by Combining Language Models With Symbolic Solvers
https://github.com/julia-liuj/nlft
공식 코드https://github.com/jwhj/oreo
공식 코드https://github.com/katiekang1998/reasoning_generalization
공식 코드What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?
https://github.com/katsumiok/askit-artifact
공식 코드AskIt: Unified Programming Interface for Programming with Large Language Models
https://github.com/katsumiok/pyaskit
공식 코드AskIt: Unified Programming Interface for Programming with Large Language Models
https://github.com/katsumiok/ts-askit
공식 코드AskIt: Unified Programming Interface for Programming with Large Language Models
https://github.com/kbsdjames/math-minos
공식 코드https://github.com/kbsdjames/omni-math
공식 코드Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
https://github.com/kbsdjames/omni-math-rule
공식 코드Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
https://github.com/kipok/nemo-skills
공식 코드OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
https://github.com/kojima-takeshi188/zero_shot_cot
공식 코드https://github.com/krafton-ai/lexico
공식 코드Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries
https://github.com/kumar-shridhar/distiiling-lm
공식 코드Distilling Reasoning Capabilities into Smaller Language Models
https://github.com/kumar-shridhar/smart
공식 코드SMART: Self-learning Meta-strategy Agent for Reasoning Tasks
https://github.com/lichenghaobuaa/tokenlearning
공식 코드Re-Initialization Token Learning for Tool-Augmented Large Language Models
https://github.com/liranringel/learning-continue-thinking-token
공식 코드Learning a Continue-Thinking Token for Enhanced Test-Time Scaling
https://github.com/longfeiyun17/diversitycollapse
공식 코드https://github.com/lotusrc/d2po
공식 코드https://github.com/lqtrung1998/mwp_cot_design
공식 코드https://github.com/lqtrung1998/mwp_reft
공식 코드https://github.com/lzhxmu/cppo
공식 코드CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models
https://github.com/mandyyyyii/east
공식 코드https://github.com/maple-research-lab/slot
공식 코드SLOT: Sample-specific Language Model Optimization at Test-time
https://github.com/mario-math-reasoning/mario
공식 코드MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
https://github.com/marwanwalid2/reasoning-vs-inferncescaling
공식 코드Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-Solving
https://github.com/mathllm/Step-Controlled_DPO
공식 코드Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.