Re:Astra can read and write weights to improve Qwen
by christopear on Tuesday November 03, 2026 @07:40AM
(#50351303)
Not quite true. In earlier examples (like Pong vs Pacman) the models are quite small and a lot of the topological structure can be read from the input weights. Whereas, in latter tasks, they give the model a harness:
> We give Astra a... Python session, like a notebook, with the model’s weights loaded. Then, we allow it to select prompts from the training sets of GSM8K and MATH to perform a budget of forward passes (average batch of 64 sequences, max 512)
It's interesting that they can now understand weight patterns, but not much more interesting than LLMs being able to read/write directly in Base64.
However, I think this likely indicates that OpenAI and Anthropic are now including model weights directly in their training - rather than an emergent property as the blog suggests. I think this is the more concerning finding - especially if doing so is in a bid to find methods for RSI.
> We give Astra a... Python session, like a notebook, with the model’s weights loaded. Then, we allow it to select prompts from the training sets of GSM8K and MATH to perform a budget of forward passes (average batch of 64 sequences, max 512)
It's interesting that they can now understand weight patterns, but not much more interesting than LLMs being able to read/write directly in Base64.
However, I think this likely indicates that OpenAI and Anthropic are now including model weights directly in their training - rather than an emergent property as the blog suggests. I think this is the more concerning finding - especially if doing so is in a bid to find methods for RSI.