DeepSeek V4.1 Flash 1017 Tested on Weight-Space Programming
A Reddit user reported that DeepSeek V4.1 Flash 1017 inferred the behavior of four small reinforcement-learning networks from rendered weights, an architecture outline and four candidate tasks. The model succeeded on CartPole, Flappy and Catch but confused BallBeam with CartPole; the author stressed that each checkpoint received only one trial, so the results do not establish reliability.
The preliminary test suggests a potentially notable capability for interpreting neural-network weights from text, but the four one-off trials and one misclassification leave its reliability unproven.
Inspired by Weight-Space Programming, I tested whether DeepSeek V4.1 Flash 1017 could figure out what a small neural network does by reading its weights.
I fed it four anonymized RL checkpoints: 51k parameters each, identical architectures, with all weights rendered as text. No metadata, network source code or execution. It received the architecture outline and four candidate tasks.
Results: CartPole ✅ Flappy ✅ Catch ✅ BallBeam ❌
It picked out patterns consistent with relative bird/gap position, paddle/ball position and angle-based balancing. It confused BallBeam with CartPole an interesting mistake because both share the same observation layout and similar balancing controls.
Very preliminary: one run per checkpoint. This suggests some ability to interpret weights, but four trials don’t establish reliability. These were API runs with maximum reasoning effort, consuming ~1.58M tokens total.
I may try more replicates and independently trained checkpoints, plus include the network code with task-revealing details removed. The poor LLM had to guess quite a lot it even assumed ReLU where the network uses tanh 😅
Code, checkpoints and run logs: https://github.com/galatolofederico/wsp
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