Hacker News: Bolt: Bootstrap Long Chain-of-Thought in LLMs Without Distillation [pdf]

Feb 8, 2025

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Source URL: https://arxiv.org/abs/2502.03860
Source: Hacker News
Title: Bolt: Bootstrap Long Chain-of-Thought in LLMs Without Distillation [pdf]

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The paper introduces BOLT, a method designed to enhance the reasoning capabilities of large language models (LLMs) by generating long chains of thought (LongCoT) without relying on knowledge distillation. The study showcases an innovative approach to improve performance on various reasoning tasks and highlights its applicability across different model scales.

Detailed Description:
The paper discusses the advancements in large language models (LLMs) and their reasoning capabilities, particularly through a new method called Bootstrap Long Chain-of-Thought (BOLT). This approach aims to empower LLMs to analyze and solve complex problems without the need for prior distillation from other advanced models or extensive human-guided training. Here are the key points:

– **Problem Addressed**: Many teams have sought to replicate the reasoning capabilities exhibited by models like OpenAI’s o1, primarily through knowledge distillation. However, this focus has led to uncertainties in systematically developing reasoning abilities across a broader spectrum of tasks.
– **Limitations of Current Methods**: Existing research mainly focuses on mathematics and, to a lesser extent, coding domains, which restricts the generalizability of findings.
– **Components of BOLT**:
– **LongCoT Data Bootstrapping**: Utilizes in-context learning from a standard instruct model to create LongCoT data, requiring minimal initial construction (only 10 examples in the experiments).
– **Supervised Finetuning**: Improves the LongCoT capabilities through additional training.
– **Online Training Refinement**: Further enhances the model’s reasoning skills through iterative refinement.
– **Model Evaluation**: The method has been tested on Llama-3.1-70B-Instruct across various model sizes (7B, 8B, 70B) and achieved outstanding results on multiple benchmarks, including Arena-Hard, MT-Bench, WildBench, ZebraLogic, and MATH500.

Overall, BOLT presents a significant advancement in LLM capabilities, enabling them to deliver enhanced reasoning processes without the complexities associated with traditional distillation methods. This can have practical implications for developers and researchers in AI, particularly in applications requiring advanced reasoning skills.

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