Summary of Treebon: Enhancing Inference-time Alignment with Speculative Tree-search and Best-of-n Sampling, by Jiahao Qiu et al.
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
by Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang
First submitted to arxiv on: 18 Oct 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 framework called TreeBoN that enhances the performance of large language models without requiring additional training or fine-tuning. The approach integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling, which generates multiple responses and selects the best one. TreeBoN maintains a set of parent nodes, iteratively branching and pruning low-quality responses to reduce computational overhead while maintaining high output quality. The framework also leverages token-level rewards from Direct Preference Optimization (DPO) to guide tree expansion and prune low-quality paths. The authors evaluate TreeBoN using various datasets, including AlpacaFarm, HH-RLHF, UltraFeedback, GSM8K, and TutorEval, demonstrating consistent improvements. Specifically, TreeBoN achieves the highest win rate of 65% on TutorEval and around 60% win rates across other different datasets, outperforming standard BoN with the same computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper finds a way to make large language models better without needing more training. They create a new approach called TreeBoN that helps these models generate high-quality answers quickly. The approach works by looking at many possible answers and choosing the best one, while also making sure it’s not too expensive to compute. They test their method using different datasets and find that it improves performance consistently. This means that TreeBoN can help large language models give more accurate and helpful responses. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Pruning » Rlhf » Token
