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..
AlphaMath Almost Zero: Process Supervision without Process
Authors: Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on both in-domain and out-of-domain datasets demonstrate that even without GPT-4 or human-annotated process supervision, our Alpha Math framework achieves comparable or superior results to previous state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Guoxin Chen , Minpeng Liao , Chengxi Li , Kai Fan Tongyi Lab EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Inference with MCTS; Algorithm 2: Step-level Beam Search |
| Open Source Code | Yes | Code: https://github.com/MARIO-Math-Reasoning/Super_MARIO |
| Open Datasets | Yes | For the training sets, we exclusively extract question and answer pairs from GSM8K [7] and MATH [15], omitting the human-annotated solution analysis. |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention or provide details for a separate validation dataset split. |
| Hardware Specification | Yes | All experiments were conducted on Ubuntu 22.04 equipped with 8 * NVIDIA A100 GPUs. |
| Software Dependencies | Yes | Our code mainly depends on Python 3.114 and Py Torch 2.1.25. ... We trained all models with Deep Speed Ze RO Stage2 [29] and Flash-Attention 2 [9]. |
| Experiment Setup | Yes | For supervised fine-tuning, we set the learning rate of 4e-5, batch size of 1024, the weight of the value loss to 0.01 or 0.0005 (for Llama3 [11]), and train the model for 10 epochs. We employ the Adam W optimizer [24] and the cosine learning rate scheduler with the warmup rate set to 0.03. Table 6 provides key hyperparameters of Alpha Math. |