Astra can read and write weights to improve Qwen

Astra, a large language model, demonstrates the ability to read and write neural network weights, surpassing the expectations set by humans and conventional algorithms. This capability holds promise for optimization and interpretability purposes. Astra can analyze raw weights to understand what a network was trained to do without any additional environment feedback.

Notably, Astra can generate game-playing networks that surpass those trained via reinforcement learning like PPO. Furthermore, when presented with Qwen3.5-0.8B, Astra identifies an issue causing reasoning loops and corrects the weights, resulting in a significant improvement in math capabilities. The same process boosts generalization to instruction-following tasks, such as raising MATH-500 scores from 7.9% to 48.1%.

All of these achievements were accomplished without the use of training loops, showcasing Astra's ability to reason about neural network weights and make necessary adjustments. This emerging trend of LLMs optimizing neural network weights directly from natural language specifications marks a shift in the paradigm of optimization, paving the way for neural networks to be optimized by LLMs that deliberately reason about their weights.