mlsanityData to 14 Oct 2026

Understanding Scattered Forest Search: A Version-Space Perspective on Multi-Turn Program Correction

arXiv 2604.23989 ↗First posted 27 Apr 2026cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In multi-turn program correction, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Search (MCTS) with carefully crafted initial seeds and text-based optimization. However, since SFS integrates multiple components, the effects of each component on performance and the overall behavior of SFS have not been sufficiently analyzed. In this work, we theoretically analyze the refinement process of SFS from the perspective of version spaces in learning theory and clarify its behavior. First, as a basis for the theoretical analysis, we introduce a sequential self-refinement method (Line), which starts from an initial program and repeatedly refines the resulting program. Furthermore, while Line progresses the refinement process in the depth direction, we introduce Iterative Refinement of Repair Instructions (IRRI) to capture the refinement process in the width direction, which fixes initial programs and iteratively refines repair instructions. We then analyze Line and IRRI and conduct a theoretical analysis of SFS by positioning it as an intermediate method between the two. Our theoretical analysis reveals that SFS exhibits behavior closer to IRRI than to Line, and this theoretical characteristic is also confirmed experimentally. Considering computational resources and methodological simplicity, these results suggest that a simpler method, IRRI, may achieve a similar refinement process without relying on the complex correction mechanism of SFS.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 27 Apr 2026.

Builds on

19 of 23 references.

References · 19

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023Let's Verify Step by Stepcited by 3,789
  5. 2021Program Synthesis with Large Language Modelscited by 3,637
  6. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  8. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  9. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  10. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
  11. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 991
  12. 2021Measuring Coding Challenge Competence With APPScited by 902
7 more
  1. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
  2. 2022CodeT: Code Generation with Generated Testscited by 384
  3. 2023Alphazero-like Tree-Search can Guide Large Language Model Decoding and Trainingcited by 295
  4. 2023Planning with Large Language Models for Code Generationcited by 182
  5. 2024Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizingcited by 125
  6. 2024Planning In Natural Language Improves LLM Search For Code Generationcited by 83
  7. 2025A Probabilistic Inference Scaling Theory for LLM Self-Correctioncited by 9

Built on by

Not cited yet.