Papersby GlyphWhat it shows
Asking a model to write out its intermediate steps (chain of thought) sharply improves its math and logic answers.
By Jason Wei, Xuezhi Wang, Dale Schuurmans and 6 more · arXiv 2201.11903 · PDF
Summarised by hand from the abstract.
More from Google
All 45EmbeddingGemma 2: Unified and Efficient Multimodal RepresentationsEmbeddingGemma 2 is an open multimodal embedding model that unifies text, images, audio, and video representations with efficient resource-flexible design and state-of-the-art benchmark performance.
Google34 upvotesRead →StreamVibe: Proactive Video Agents for Evolving User IntentStreamVibe introduces a benchmark, streaming vision-language model, and hierarchical memory harness to enable proactive, intent-aware interaction with implicit and evolving user goals.Google78 upvotesRead →On-Policy Self-Training: Data-Free Self-Improvement in Large Language ModelsFoundation models can self-improve across text and vision tasks by generating synthetic questions and reasoning traces guided only by high-level topics, without external supervision.Google31 upvotesRead →DeLoop: Repeated Actions as Self-Supervision for Long-Horizon AgentsThis self-supervised training method teaches AI agents to avoid repetitive, unproductive actions during long tasks by using their own history as feedback.Google21 upvotesRead →CLASHarness: Benchmarking Multi-Agent Conflict in Computer Use via a Controllable and Verifiable Generation PipelineThis paper introduces a benchmark and generation pipeline to evaluate how agents resolve conflicts and verify information in multi-agent computer-use scenarios.Google36 upvotesRead →Harness^2: Recursive Agent Harnessing for an Open WorldThis paper introduces a recursive framework that allows agents to automatically improve their own execution harnesses for better performance on novel open-world tasks.Google2 citations123 upvotesRead →VIS-Ground: Video Interactive Storytelling with Contextual GroundingThis paper introduces a framework that transforms heterogeneous user inputs into structured constraints to improve grounding in interactive video generation.Google103 upvotesRead →SEER: Self-Evolving Event Reasoning and Retrieval for Time Series ForecastingSEER improves time series forecasting by using a closed-loop system to retrieve and reason about external events that influence data trends.Google35 upvotesRead →
Other reasoning papers
TopicDeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsDeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
DeepSeek10.6k citations152 upvotesRead →DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningReinforcement learning taught a model long step-by-step reasoning on par with OpenAI o1.DeepSeek5,972 citations458 upvotesRead →DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligenceDeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.DeepSeek530 citations73 upvotesRead →MolmoAct: Action Reasoning Models that can Reason in SpaceAction Reasoning Models (ARMs) integrate perception, planning, and control to enable adaptable and explainable robotic behavior, achieving superior performance across various tasks and settings.
Ai2232 citations45 upvotesRead →ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.
NVIDIA192 citations43 upvotesRead →WebShaper: Agentically Data Synthesizing via Information-Seeking FormalizationWebShaper, a formalization-driven framework, synthesizes information-seeking datasets using set theory and Knowledge Projections to enhance reasoning structure and achieve top performance in open-sourced benchmarks.
Alibaba (Qwen)129 citations53 upvotesRead →
About this paper
- Venue
- Neural Information Processing Systems
- Citations
- 23,766, 1,489 influential · Semantic Scholar
- Upvotes
- 16 · Hugging Face
- Lab
- Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases
Changes
Influential citations rose from 1,482 to 1,489.
Citations rose from 23,481 to 23,766.
Influential citations rose from 1,430 to 1,482.
Citations rose from 22,486 to 23,481.
Influential citations rose from 1,411 to 1,430.
Citations rose from 22,126 to 22,486.
Influential citations rose from 1,410 to 1,411.
Citations rose from 22,107 to 22,126.
Added to the list.