The 'Astra can read and write weights to improve Qwen' post is misleading
- ID
- 42572
- Status
- summarized
- Published
- 03 Nov 2026, 8:21 PM
- Fetched
- 04 Nov 2026, 12:27 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://honnibal.dev/blog/astra-read-write-misleading
- Source URL
- https://hnrss.org/best
Summary
- Score
- 8.0
- Created
- 04 Nov 2026, 12:28 AM
- Tags
- Audience
- developersai_ml_learnersai_agent_usersvibe_coders
What happened
This post pushes back on the viral 'Astra can read and write weights to improve Qwen' claim, arguing that the headline conflates several different experiments. In the article's table, the Qwen3.5-0.8B MATH-500 jump from 7.9% to 48.1% comes from an experiment that does not read raw weights, but does write weights, writes Python, and runs the network for 50 passes. It also notes mixed game-network results: one network beats PPO-100M but loses to PPO-500M on Seaquest (53 vs 89) and Asterix (53 vs 55).
Why it matters
If you saw the Astra/Qwen claim and were about to treat it as evidence that a model can improve another model 'just by looking at raw weights', check the method first: this Qwen result required weight edits plus Python and 50 network runs, so it is not a no-tool, no-feedback capability. For AI/ML learners and agent builders, the practical move is to ask for per-experiment ablations (raw floats vs Python vs running the net) before repeating the claim in a demo, roadmap, or talk. There is no direct Malaysia/SEA angle in the text.
Discussion angle
What minimum evidence should we require before repeating 'an LLM edited its own weights'—raw floats in context, generated Python, or 50 passes of running the network?