Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Paper
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2312.04474
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Published
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29
llm_reasoning
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Boosting LLM Reasoning: Push the Limits of Few-shot Learning with Reinforced In-Context Pruning
Paper • 2312.08901 • Published -
Learning From Mistakes Makes LLM Better Reasoner
Paper • 2310.20689 • Published • 28 -
Making Large Language Models Better Reasoners with Step-Aware Verifier
Paper • 2206.02336 • Published • 1 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 39 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 28 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 109 -
Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 27 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 73 -
V-STaR: Training Verifiers for Self-Taught Reasoners
Paper • 2402.06457 • Published • 8 -
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Paper • 2406.12050 • Published • 18 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 9 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 33 -
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Paper • 2407.21787 • Published • 3 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 13 -
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
Paper • 2406.06592 • Published • 25 -
Large Language Models Can Self-Improve in Long-context Reasoning
Paper • 2411.08147 • Published • 59