Post-training is making models better at benchmarks. They seem more consistent at coding, but dumber to me and I want to know what y'all are noticing.
Here are the big three I'm noticing (mostly in claude): 1) Agents cannot write natural language well. Claude uses vague phrases to avoid any technical term such as "What the caller calls" instead of saying "callee". 2) Agents cannot manage encapsulation; they always make spaghetti code. 3) Agents are afraid to break anything. They always add more code to work around the code that's already there rather than restructuring it.
No amount of rules or coaching helps much. Ask an agent to make code simpler and it usually gets longer. Add any sort of rule, and the agent will follow it like you just got a wish from the monkey's paw.
Has it always been this way or is post-training compressing the output to predictable failure modes?
Comments URL: https://news.ycombinator.com/item?id=50502344
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