Summary of Step-level Value Preference Optimization For Mathematical Reasoning, by Guoxin Chen et al.
Step-level Value Preference Optimization for Mathematical Reasoning
by Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to fine-tuning large language models (LLMs) for complex multi-step reasoning tasks, such as mathematical reasoning, is introduced in this paper. The proposed method, Step-level Value Preference Optimization (SVPO), employs Monte Carlo Tree Search (MCTS) to automatically annotate step-level preferences for these tasks. This allows for more accurate fine-tuning of the LLM using Direct Preference Optimization (DPO). Additionally, an explicit value model is trained to replicate the behavior of the implicit reward model, enabling the generation of higher-reward responses with minimal cost during inference. The paper demonstrates state-of-the-art performance on both in-domain and out-of-domain mathematical reasoning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to improve large language models for complex tasks like math problems. It’s called Step-level Value Preference Optimization, or SVPO for short. SVPO uses a special search method called Monte Carlo Tree Search to help the model understand what makes good answers in math. This helps fine-tune the model so it gives better answers with less effort. The researchers tested their method and found it worked really well on math problems. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Optimization
