ByteDance Finds the Reason Behind DeepSeek's Inconsistent Performance

The ByteDance team investigated the phenomenon of significant output quality fluctuations in DeepSeek models on the same tasks and found that the key factor lies in token positioning within the context. This means that the quality of a model's answer to the same question depends not only on the question itself but also on the arrangement positions of tokens in the input text.

The token positioning issue essentially involves the positional encoding mechanism of large language models. Current mainstream models, such as the Transformer architecture, rely on positional encoding to distinguish token order, but the accuracy and generalization ability of positional encoding may vary across different context lengths, leading to unstable reasoning capabilities under specific token arrangements.

This finding has important reference value for large model engineering practices. In actual deployment, adjusting prompt structures and optimizing token arrangements may improve output stability without changing model weights, providing new pathways for reducing inference costs and improving reliability.

As an open-source model, DeepSeek's performance fluctuations have also drawn community attention to model consistency. If ByteDance's research is further published, it could advance the industry's understanding and improvement of large model inference stability.

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