FISolver trains a compact LLM on backward-generated (differential equation, first integral) pairs and uses guided reinforcement learning to outperform larger models and Mathematica on first-integral benchmarks at lower cost.
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WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
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abstract
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, most existing open-source models are only pre-trained on large-scale internet data and without math-related optimization. In this paper, we present WizardMath, which enhances the mathematical CoT reasoning abilities of LLMs without using external python tools, by applying our proposed Reinforcement Learning from Evol-Instruct Feedback (RLEIF) method to the domain of math. Through extensive experiments on two mathematical reasoning benchmarks, namely GSM8k and MATH, we reveal the extraordinary capabilities of our model. Remarkably, WizardMath-Mistral 7B surpasses top-tier open-source LLMs by a substantial margin with higher data efficiency. Furthermore, WizardMath 70B even outperforms GPT-3.5-Turbo, Claude 2, Gemini Pro and GPT-4-early-version. Additionally, our preliminary exploration highlights the pivotal role of instruction evolution and process supervision in achieving exceptional math performance. For more details refer to https://github.com/nlpxucan/WizardLM
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representative citing papers
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FISolver trains a compact LLM on backward-generated (differential equation, first integral) pairs and uses guided reinforcement learning to outperform larger models and Mathematica on first-integral benchmarks at lower cost.
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Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories
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CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
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Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models
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MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
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Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning
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DEL: Digit Entropy Loss for Numerical Learning of Large Language Models
DEL is a new loss for LLM numerical learning that applies supervised digit entropy optimization and extends to floating-point numbers, showing improved accuracy and distance metrics over prior methods on math benchmarks.
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Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
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Distribution Corrected Offline Data Distillation for Large Language Models
A distribution-correction framework for offline LLM reasoning distillation improves accuracy on math benchmarks by adaptively aligning teacher supervision with the student's inference-time distribution.
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CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference
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Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
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MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
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Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs
Step-DPO performs preference optimization on individual reasoning steps rather than complete answers, producing nearly 3% accuracy gains on MATH for 70B+ parameter models with 10K preference pairs.
-
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
Math-Shepherd is an automatically trained process reward model that scores solution steps to verify and reinforce LLMs, lifting Mistral-7B from 77.9% to 89.1% on GSM8K and 28.6% to 43.5% on MATH.
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Llemma: An Open Language Model For Mathematics
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MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
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DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
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From System 1 to System 2: A Survey of Reasoning Large Language Models
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