DeepSeek's R1-Zero and R1 models demonstrate impressive performance in language modeling, outperforming open-source models of comparable size in several benchmarks. R1-Zero, despite being pre-trained on only 1.5 trillion tokens, achieves similar performance to much larger open-source models trained on 3-4 trillion tokens. The more powerful R1 model, trained with selected data and reinforcement learning from human feedback, further improves upon R1-Zero, especially in reasoning and following instructions. DeepSeek attributes its success to a combination of improved architecture, efficient training, and high-quality data. The results highlight the potential for achieving high performance with smaller, more efficiently trained models.
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Summary of Comments ( 94 )
https://news.ycombinator.com/item?id=42868390
HN commenters discuss the implications of DeepSeek's impressive results in the ARC (Abstraction and Reasoning Corpus) challenge with their R1-Zero and R1 models. Several highlight the significance of achieving near-perfect scores on the training set, raising questions about the nature of generalization and the potential limitations of current evaluation metrics. Some express skepticism about the actual novelty of the approach, noting similarities to existing techniques and questioning the impact of architectural choices versus data augmentation. The closed nature of DeepSeek and the lack of publicly available code also draw criticism, with some suspecting potential overfitting or undisclosed tricks. Others emphasize the importance of reproducible research and open collaboration for scientific progress in the field. The potential for such powerful models in practical applications is acknowledged, with some speculating on future developments and the need for better benchmarks.
The Hacker News post titled "An analysis of DeepSeek's R1-Zero and R1" with the link provided has a modest number of comments discussing the implications of DeepSeek's performance in the retrieval challenge. Many commenters focus on the nuances of evaluating retrieval models and the trade-offs between different approaches.
Several commenters highlight the importance of considering the cost of retrieval alongside effectiveness. One commenter points out that the blog post doesn't mention cost, which they find surprising given the importance of cost-effectiveness in real-world applications. Another commenter echoes this sentiment, suggesting that evaluating retrieval solely on effectiveness metrics without considering cost is misleading. This commenter goes on to argue that retrieval should be viewed as an optimization problem balancing cost and effectiveness, making the analogy to self-driving cars where perfect navigation is useless if it takes an unreasonable amount of time.
Another thread of discussion revolves around the specifics of the retrieval task and the appropriateness of different evaluation metrics. One commenter questions the choice of nDCG@10 as the primary metric, suggesting that other metrics might be more informative for specific use cases. This sparks a discussion about the limitations of nDCG and the need to consider the distribution of relevant documents.
The conversation also touches on the open-source nature of the models. While DeepSeek has not yet open-sourced their models, some commenters express hope that they will do so in the future, contributing to the advancement of open retrieval models. One commenter specifically mentions their surprise and hope, given the generally open-source tendencies of similar models from research institutions.
A few commenters delve into the technical details of the models, discussing the trade-offs between dense and sparse retrieval methods. One commenter argues that the blog post overstates the effectiveness of dense retrieval, pointing to the continued strong performance of sparse methods. This leads to a discussion about the specific strengths and weaknesses of each approach.
Finally, some commenters offer their perspectives on the broader implications of DeepSeek's results. One commenter speculates about the potential impact on the search industry, suggesting that these advancements could lead to more efficient and effective search engines.
Overall, the comments on Hacker News reflect a thoughtful engagement with the topic of retrieval models, highlighting the importance of considering factors beyond raw effectiveness scores, such as cost and the specifics of the retrieval task. The discussion also reveals the ongoing debate within the community about the relative merits of different retrieval approaches.