THOUGHTSCULPT: Reasoning with Intermediate Revision and Search
On the map: LLM reasoning → Test-time compute and efficient thinking
Abstract (arXiv)
We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heuristic, which in practice is often simply an LLM evaluator. Critically, our action space includes revision actions: THOUGHTSCULPT may choose to revise part of its previous output rather than continuing to build the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art reasoning methods across three challenging tasks: Story Outline Improvement (up to +30% interestingness), Mini-Crosswords Solving (up to +16% word success rate), and Constrained Generation (up to +10% concept coverage).
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 32 of its 37 references that are in the map. First posted on arXiv 9 Apr 2024.
Builds on
32 of 37 references.
Closest ancestors · 4
Same direction, newest first
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Models17 May
- 2023Self-Evaluation Guided Beam Search for Reasoning1 May
- 2023REFINER: Reasoning Feedback on Intermediate Representations4 Apr
- 2023Self-Refine: Iterative Refinement with Self-Feedback30 Mar
Other references · 28
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- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
16 more
- 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
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- 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
- 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 396
- 2023Language Models can Solve Computer Taskscited by 371
- 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
- 2022Generating Sequences by Learning to Self-Correctcited by 271
- 2022Quark: Controllable Text Generation with Reinforced Unlearningcited by 236
- 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2022Rainier: Reinforced Knowledge Introspector for Commonsense Question Answeringcited by 43
- 2021Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedbackcited by 35
- 2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
- 2023Decomposing Complex Queries for Tip-of-the-tongue Retrievalcited by 24
- 2022DOC: Improving Long Story Coherence With Detailed Outline Controlcited by 11
- 2024RoT: Enhancing Large Language Models with Reflection on Search Treescited by 11
Built on by
Citing papers per year: 2024: 4; 2025: 5; 2026: 2.
11 papers.
- 2024From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Modelscited by 136
- 2024WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Explorationcited by 100
- 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
- 2024Scaling LLM Inference with Optimized Sample Compute Allocationcited by 16
- 2025From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuningcited by 12
- 2025Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimizationcited by 9
- 2024Towards Building Specialized Generalist AI with System 1 and System 2 Fusioncited by 8
- 2025Hierarchical Vision Language Action Model Using Success and Failure Demonstrationscited by 2
- 2025CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoningcited by 0
- 2026Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agentscited by 0
- 2026The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modescited by 0
- 2026The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes9 Jun 2026
- 2026Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents20 Jan 2026
- 2025Hierarchical Vision Language Action Model Using Success and Failure Demonstrations3 Dec 2025
- 2025Large Language Models for Planning: A Comprehensive and Systematic Survey26 May 2025
- 2025CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoning24 Mar 2025
- 2025Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization24 Feb 2025
- 2025From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning21 Jan 2025
- 2024Scaling LLM Inference with Optimized Sample Compute Allocation29 Oct 2024
- 2024WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration28 Aug 2024
- 2024Towards Building Specialized Generalist AI with System 1 and System 2 Fusion11 Jul 2024
- 2024From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models24 Jun 2024
Similar, not cited
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- 2026Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
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- 2024RATT: A Thought Structure for Coherent and Correct LLM Reasoning
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