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.
DeepSeek has released the R1 "Dynamic," a 1.58-bit inference AI chip designed for large language models (LLMs). It boasts 3x the inference performance and half the cost compared to the A100. Key features include flexible tensor cores, dynamic sparsity support, and high-speed networking. This allows for efficient handling of various LLM sizes and optimization across different sparsity patterns, leading to improved performance and reduced power consumption. The chip is designed for both training and inference, offering a competitive solution for deploying large-scale AI models.
Hacker News users discussed DeepSeekR1 Dynamic's impressive compression ratios, questioning whether the claimed 1.58 bits per token was a true measure of compression, since it included model size. Some argued that the metric was misleading and preferred comparisons based on encoded size alone. Others highlighted the potential of the model, especially for specialized tasks and languages beyond English, and appreciated the accompanying technical details and code provided by the authors. A few expressed concern about reproducibility and potential overfitting to the specific dataset used. Several commenters also debated the practical implications of the compression, including its impact on inference speed and memory usage.
The Hacker News post asks if anyone is working on interesting projects using small language models (LLMs). The author is curious about applications beyond the typical large language model use cases, specifically focusing on smaller, more resource-efficient models that could run on personal devices. They are interested in exploring the potential of these compact LLMs for tasks like personal assistants, offline use, and embedded systems, highlighting the benefits of reduced latency, increased privacy, and lower operational costs.
HN users discuss various applications of small language models (SLMs). Several highlight the benefits of SLMs for on-device processing, citing improved privacy, reduced latency, and offline functionality. Specific use cases mentioned include grammar and style checking, code generation within specialized domains, personalized chatbots, and information retrieval from personal documents. Some users point to quantized models and efficient architectures like llama.cpp as enabling technologies. Others caution that while promising, SLMs still face limitations in performance compared to larger models, particularly in tasks requiring complex reasoning or broad knowledge. There's a general sense of optimism about the potential of SLMs, with several users expressing interest in exploring and contributing to this field.
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.