Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
Authors: Ziyu Wan, Xidong Feng, Muning Wen, Stephen Marcus Mcaleer, Ying Wen, Weinan Zhang, Jun Wang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results across reasoning, planning, alignment, and decision-making tasks show that TS-LLM outperforms existing approaches and can handle trees with a depth of 64. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University 2University College London 3Carnegie Mellon University. |
| Pseudocode | Yes | Algorithm 1 Simplied MCTS Simulation; Algorithm 2 MCTS-Rollout; Algorithm 3 MCTS-α |
| Open Source Code | Yes | Our code is open-sourced at https://github.com/waterhorse1/LLM_Tree_Search. |
| Open Datasets | Yes | GSM8k (Cobbe et al., 2021), Game24 (Yao et al., 2023), Pr Onto QA (Saparov & He, 2022), RLHF alignment task using synthetic RLHF data (Dahoas), and chess endgames (Abdulhai et al., 2023). |
| Dataset Splits | Yes | We split the dataset to 30000/3000 as training and test set respectively. |
| Hardware Specification | Yes | The experiments were conducted on the same machine with 8 NVIDIA A800 GPUs, the CPU information is Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz. |
| Software Dependencies | No | The paper mentions models like LLaMA2-7B and GPT-2-small, but does not specify software dependencies with version numbers (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | The training is conducted on 8 NVIDIA A800 GPUs, using a cosine scheduler decaying from lr=2e-5 to 0.0 with a warmup ratio of 0.03, batch size 128 for 3 epochs. We set temperature=1.0, top p=1.0, top k=100 when using LLM to generate tree actions. |