Summary of Inference-aware Fine-tuning For Best-of-n Sampling in Large Language Models, by Yinlam Chow et al.
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
by Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, Aleksandra Faust
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a novel approach to fine-tuning large language models (LLMs) for better performance at inference time. The authors introduce a paradigm that directly optimizes the performance of an inference-time strategy, specifically the Best-of-N (BoN) method, which selects the best response from multiple LLM-generated options. To achieve this, they develop imitation learning and reinforcement learning methods to overcome the non-differentiable argmax operator in BoN. The authors demonstrate that their approach leads to improved performance and reduced inference-time compute, with significant gains on benchmarks such as Hendrycks MATH and HumanEval. Their results show that the BoN-aware models implicitly learn a meta-strategy that balances exploration and exploitation, leading to better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models work better at predicting answers. Right now, these models are really good at understanding what we mean when we ask them questions, but they’re not as good at giving us the best answer. To fix this, the researchers came up with a new way to train these models so that they pick the best response from many possible answers. They tested their method and found that it works really well! The results show that their approach can help language models give better answers more quickly. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Reinforcement learning
