Researchers embed coding agents in DeepSeek training

Researchers explore placing coding agents inside neural network training loops to perform mechanistic interventions, allowing models to redesign their own architectures and training components on the fly. In experiments across nanochat, CLRS-Text, and DeepSeek models, this technique improved sample efficiency, addressed algorithmic generalisation gaps, and boosted math competition accuracy. Open article →