Unverified50% confidenceOpinionExact time
OpenAI researcher Noam Brown warned in an interview that as large language models become increasingly powerful, they may face a bottleneck where sufficiently difficult problems cannot be found to train them
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9/28/2026
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UnverifiedBrown believes that the core of solving Millennium Prize Problems is not a multi-agent architecture, but the general and powerful model trained by OpenAI, and stated he would not attribute 10% of the credit to multi-agent systems.70% similarUnverifiedBrown's perspective focuses on the difficulty wall in the reinforcement learning phase—namely the lack of sufficiently difficult data that can generate learning value—rather than a lack of data itself69% similarUnverifiedBrown candidly acknowledged that directly translating experimental results into improvements in alignment capability still faces many challenges and is not an immediate solution.67% similarUnverifiedLarge language models have no genuine introspective access to their own execution traces and can only reconstruct a plausible-looking narrative based on context and training tendencies66% similarUnverifiedFor complex tasks requiring open-ended research and deep cross-page reasoning, small models are difficult to match even after fine-tuning, making large models indispensable.65% similar
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