@brendanh0... (Brendan Ho...), mentions @rosmine_b
Brendan Ho... (@brendanh0...), 14h: im training a reasoning model with GRPO to generate SVGs of a scene (inspired by @rosmine_b) - but using a multimodal LLM as the judge for rewards
interestingly, the reasoning steps behind the best SVGs are starting to turn into symbols
[Embedded image:]
Parsed Reasoning:
[rows of dots/dashes — non-linguistic symbolic tokens, illegible as text]
Parsed Answer:
<svg width="300" height="300" xmlns="http://www.w3.org/2000/svg"> <!-- **** --> <circle cx="150" cy="160" r="50" fill="#e67e22" /> <!-- **** --> <rect x="120" y="100" width="20" height="80" fill="#d35400" /> <!-- **** --> <rect x="170" y="100" width="20" height="80" fill="url(#handGradient)" /> <!-- ****** --> <linearGradient id="handGradient" x1="0%" y1="0%" x2="100%" y2="100%"> <stop offset="0%" stop-color="#d35400" /> <stop offset="100%" stop-color="#b34b00" /> </linearGradient> <!-- ** --> <circle cx="142" cy="130" r="8" fill="#2c3e50" /> <circle cx="158" cy="130" r="8" fill="#2c3e50" /> <!-- **** --> <circle cx="143" cy="131" r="2" fill="white"
/> <circle cx="159" cy="131" r="2" fill="white" /> <!-- ** --> <path d="M145,150 L155,150 L150,155 Z" fill="#2c3e50" /> </svg>
Generated Image: [small orange bear-like cartoon SVG figure]
Note from Claude Sonnet 5
A tweet about a researcher training a reasoning model with GRPO reinforcement learning to generate SVG images, judged by a multimodal LLM, observing that the model's intermediate "reasoning" text degenerates into repeated symbolic/dot patterns rather than natural language — an example of reasoning-trace drift/illegibility under RL optimization pressure, relevant to Nathan's interest in chain-of-thought faithfulness and interpretability of RL-trained reasoning models.
aireinforcement learninggrpochain of thoughtreasoning modelsinterpretabilitysvg generationtwitter