-
Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
Paper • 2408.07199 • Published • 21 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 75 -
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Paper • 2406.12050 • Published • 19 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 10
Collections
Discover the best community collections!
Collections including paper arxiv:2408.03314
-
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 146 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 12 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 53 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 45
-
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 176 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 63 -
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
Paper • 2410.02884 • Published • 53 -
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
Paper • 2311.07445 • Published
-
Solving math word problems with process- and outcome-based feedback
Paper • 2211.14275 • Published • 8 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
Paper • 2404.05221 • Published • 1
-
STaR: Bootstrapping Reasoning With Reasoning
Paper • 2203.14465 • Published • 8 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 10 -
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 71 -
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Paper • 2411.14405 • Published • 58
-
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 71 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
ICAL: Continual Learning of Multimodal Agents by Transforming Trajectories into Actionable Insights
Paper • 2406.14596 • Published • 5 -
A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More
Paper • 2407.16216 • Published
-
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 71 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
Solving math word problems with process- and outcome-based feedback
Paper • 2211.14275 • Published • 8
-
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Paper • 2408.06195 • Published • 66 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 136 -
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 54 -
Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
Paper • 2405.06682 • Published • 3