LIMO (Less Is More for Reasoning) introduces a new approach to improve the reasoning capabilities of large language models (LLMs). It argues that current chain-of-thought (CoT) prompting methods, while effective, suffer from redundancy and hallucination. LIMO proposes a more concise prompting strategy focused on extracting only the most crucial reasoning steps, thereby reducing the computational burden and improving accuracy. This is achieved by training a "reasoning teacher" model to select the minimal set of effective reasoning steps from a larger CoT generated by another "reasoning student" model. Experiments demonstrate that LIMO achieves better performance than standard CoT prompting on various reasoning tasks, including arithmetic, commonsense, and symbolic reasoning, while also being more efficient in terms of both prompt length and inference time. The method showcases the potential of focusing on essential reasoning steps for enhanced performance in complex reasoning tasks.
The blog post "Effective AI code suggestions: less is more" argues that shorter, more focused AI code suggestions are more beneficial to developers than large, complete code blocks. While large suggestions might seem helpful at first glance, they're often harder to understand, integrate, and verify, disrupting the developer's flow. Smaller suggestions, on the other hand, allow developers to maintain control and understanding of their code, facilitating easier integration and debugging. This approach promotes learning and empowers developers to build upon the AI's suggestions rather than passively accepting large, opaque code chunks. The post further emphasizes the importance of providing context to the AI through clear prompts and selecting the appropriate suggestion size for the specific task.
HN commenters generally agree with the article's premise that smaller, more focused AI code suggestions are more helpful than large, complex ones. Several users point out that this mirrors good human code review practices, emphasizing clarity and avoiding large, disruptive changes. Some commenters discuss the potential for LLMs to improve in suggesting smaller changes by better understanding context and intent. One commenter expresses skepticism, suggesting that LLMs fundamentally lack the understanding to suggest good code changes, and argues for focusing on tools that improve code comprehension instead. Others mention the usefulness of LLMs for generating boilerplate or repetitive code, even if larger suggestions are less effective for complex tasks. There's also a brief discussion of the importance of unit tests in mitigating the risk of incorporating incorrect AI-generated code.
Summary of Comments ( 57 )
https://news.ycombinator.com/item?id=42991676
Several Hacker News commenters express skepticism about the claims made in the LIMO paper. Some question the novelty, arguing that the core idea of simplifying prompts isn't new and has been explored in prior work. Others point out potential weaknesses in the evaluation methodology, suggesting that the chosen tasks might be too specific or not representative of real-world scenarios. A few commenters find the approach interesting but call for further research and more robust evaluation on diverse datasets to validate the claims of improved reasoning ability. There's also discussion about the practical implications, with some wondering if the gains in performance justify the added complexity of the proposed method.
The Hacker News post titled "LIMO: Less Is More for Reasoning" (https://news.ycombinator.com/item?id=42991676) discussing the arXiv paper "Less Is More for Alignment" has a limited number of comments, primarily focusing on clarification and skepticism.
One commenter asks for clarification about the meaning of "less is more" in this context, wondering if it refers to model size, the amount of training data, or something else. They also express concern that the abstract uses vague terms and wonder if there are concrete, measurable metrics for success.
Another commenter responds, explaining that "less" likely refers to smaller models and that the paper explores how better reasoning can emerge when these smaller models have a restricted view of context, especially in mathematical reasoning tasks. They suggest this might be because the limited context allows the model to focus on relevant information, improving its deduction capabilities. However, they also mention the authors acknowledge these benefits primarily apply to "mathematical reasoning-like tasks" and aren't necessarily generalizable.
A third commenter expresses skepticism towards the paper's methodology, noting the specific choice of dataset (GSM8K) and questioning how applicable the findings are to other types of problems. They highlight that GSM8K primarily tests whether a model can correctly perform a sequence of arithmetic operations and propose that the limited context simply helps the model to avoid getting overwhelmed by extraneous information in this specific scenario. They imply this doesn't necessarily demonstrate a genuine improvement in reasoning abilities.
The remaining comments are brief, with one user sharing a related paper and another providing a concise summary of the main idea presented in the LIMO paper.
In summary, the discussion revolves around understanding the "less is more" concept in the context of the paper, specifically regarding model size and context window. There's also notable skepticism about the general applicability of the findings, with concerns raised about the choice of dataset and whether the improvements observed are truly indicative of better reasoning or simply an artifact of the task's specific structure. The overall tone is one of cautious interest with a desire for more clarity and broader validation.