---
title: "Toward Self-Improvement of LLMs via Imagination, Searching,"
description: "The authors propose ALPHALLM, a self-improvement framework inspired by AlphaGo. 64 references, cited by 73 papers here."
authors: ["Ye Tian", "Baolin Peng", "Linfeng Song", "Lifeng Jin", "Dian Yu", "Haitao Mi", "Dong Yu"]
year: 2024
venue: "arXiv:2404.12253"
source: "https://arxiv.org/abs/2404.12253"
doi: "10.48550/arxiv.2404.12253"
arxiv: "2404.12253"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tian-2024-toward-self-improvement-llms-imagination/"
---
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# Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

**Authors:** [Ye Tian](https://md.research.elamur.ai/free/en/authors/ye-tian/), [Baolin Peng](https://md.research.elamur.ai/free/en/authors/baolin-peng/), [Linfeng Song](https://md.research.elamur.ai/free/en/authors/linfeng-song/), [Lifeng Jin](https://md.research.elamur.ai/free/en/authors/lifeng-jin/), [Dian Yu](https://md.research.elamur.ai/free/en/authors/dian-yu/), [Haitao Mi](https://md.research.elamur.ai/free/en/authors/haitao-mi/) and [Dong Yu](https://md.research.elamur.ai/free/en/authors/dong-yu/) · **Year:** 2024 · **Venue:** [arXiv:2404.12253](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ([NeurIPS 2024](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2024/)) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2404.12253) · [PDF](https://arxiv.org/pdf/2404.12253) · [DOI 10.48550/arxiv.2404.12253](https://doi.org/10.48550/arxiv.2404.12253) · [arXiv 2404.12253](https://arxiv.org/abs/2404.12253) · [OpenAlex](https://openalex.org/W4415797522)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [73](https://md.research.elamur.ai/free/en/papers/tian-2024-toward-self-improvement-llms-imagination/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models still struggle with multi-step reasoning and planning, and improving them usually requires large amounts of curated supervised data. The authors propose ALPHALLM, a self-improvement framework inspired by AlphaGo. It pairs an LLM with Monte Carlo Tree Search (MCTS) and needs no new human-written rationales. The framework has three parts. First, an "imagination" step synthesizes new training prompts to offset data scarcity. Second, ηMCTS is a search procedure adapted to text. It treats groups of tokens as options, merges similar states and adjusts branching adaptively, which makes the search tree shallower and cheaper. Third, three critics guide the search: a value function, a process reward model and an outcome reward model, with the outcome model able to call tools. The highest-reward trajectories found by the search are then used to fine-tune the policy. On math benchmarks, the approach lifts Llama-2-70b on GSM8K and WizardMath-70B-V1.0 on MATH to roughly GPT-4 level. This suggests that search-driven feedback can replace part of the expensive annotation work in LLM training.

## Key points
- With ηMCTS decoding, GSM8K accuracy rises from 57.8 to 92.0 (base: Llama-2-70b), and MATH accuracy rises from 20.7 to 51.0 (base: WizardMath-70B-V1.0). The reported GPT-4 scores are 92.0 and 42.5.
- Training uses 7.5k final answers for the policy and 7.5k for the critics, without any rationales. Under greedy decoding, the self-improved model reaches 73.7 on GSM8K and 23.6 on MATH.
- In the GSM8K ablation, accuracy grows step by step from 79.5 to 88.9 as components are added: adaptive branching, PRM, fast-rollout ORM, state merge and more rollouts.
- On MATH, the option-level formulation raises accuracy from 44.1 to 45.4 while cutting rollouts from 198 to 148. Dropping the tool-augmented ORM lowers accuracy to 38.8.
- Two rounds of iterative self-improvement keep raising performance, and ηMCTS-collected trajectories beat reranking-collected ones. The work appeared at NeurIPS 2024, and the code is released on GitHub (YeTianJHU/AlphaLLM).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi and Dong Yu. 2024. Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing. arXiv:2404.12253.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{tian2024toward,
  title = {{Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing}},
  author = {Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi and Dong Yu},
  year = {2024},
  doi = {10.48550/arxiv.2404.12253},
  eprint = {2404.12253},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2404.12253}
}
```

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*Updated 2 November 2026*