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GSM8K

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sanyalsunny111/flow_finetuning

공식 코드

Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting

https://github.com/scalingintelligence/large_language_monkeys

공식 코드

Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

https://github.com/scottviteri/markoviantraining

공식 코드

Markovian Transformers for Informative Language Modeling

https://github.com/secureaiautonomylab/ma-tot

공식 코드

Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent

https://github.com/shahriargolchin/dcq

공식 코드

Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models

https://github.com/soistesimmer/fetch

공식 코드

Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls

https://github.com/squeezeailab/llm2llm

공식 코드

LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

https://github.com/synthlabsai/big-math

공식 코드

Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models

https://github.com/teachable-ai-lab/tutor_gym

공식 코드

TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students

https://github.com/tengwang0318/hierarchial_reward_model

공식 코드

Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models

https://github.com/tengxiao1/gsil

공식 코드

How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

https://github.com/tergelmunkhbat/concise-reasoning

공식 코드

Self-Training Elicits Concise Reasoning in Large Language Models

https://github.com/thudm/chatglm-6b

공식 코드

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

https://github.com/tongjingqi/MathTrap

공식 코드

Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning

https://github.com/trotsky1997/mathblackbox

공식 코드

Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

https://github.com/ulab-uiuc/iranker

공식 코드

IRanker: Towards Ranking Foundation Model

https://github.com/umass-embodied-agi/commvq

공식 코드

CommVQ: Commutative Vector Quantization for KV Cache Compression

https://github.com/utmathgroup/utmath

공식 코드

UTMath: Math Evaluation with Unit Test via Reasoning-to-Coding Thoughts

https://github.com/vainf/thinkless

공식 코드

Thinkless: LLM Learns When to Think

https://github.com/vaskarnathscale/goal-conditioned-rm

공식 코드

Learning Goal-Conditioned Representations for Language Reward Models

https://github.com/vfleaking/ptst

공식 코드

Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates

https://github.com/vita-group/seal

공식 코드

SEAL: Steerable Reasoning Calibration of Large Language Models for Free

https://github.com/vividitytech/math_lm_rl

공식 코드

A mixed policy to improve performance of language models on math problems

https://github.com/wangxinyilinda/concept-based-demonstration-selection

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Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

https://github.com/whu-zqh/dup

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Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

https://github.com/woooodyy/llm-reverse-curriculum-rl

공식 코드

Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning

https://github.com/woooodyy/self-polish

공식 코드

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement

https://github.com/xnhyacinth/nesycd

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Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks

https://github.com/xuekai-zhu/pad

공식 코드

PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning

https://github.com/xuzhao0/model-selection-reasoning

공식 코드

Automatic Model Selection with Large Language Models for Reasoning

https://github.com/xwin-lm/xwin-lm

공식 코드

Common 7B Language Models Already Possess Strong Math Capabilities

https://github.com/yangling0818/supercorrect-llm

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SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights

https://github.com/yangzhch6/alignedcot

공식 코드

AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations

https://github.com/yao8839836/cp

공식 코드

Large Language Models are Contrastive Reasoners

https://github.com/yc1999/LLM2

공식 코드

LLM2: Let Large Language Models Harness System 2 Reasoning

https://github.com/yetianjhu/alphallm

공식 코드

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

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