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Danny

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upvoted a changelog about 10 hours ago
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upvoted a changelog 12 days ago
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reacted to salma-remyx's post with 🔥 23 days ago
The space of possible improvements for your AI model is large while evaluation is costly. So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback." The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives. Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth. The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis. Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter. So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation arXiv: https://arxiv.org/pdf/2510.17671 Substack: https://remyxai.substack.com/p/lilo-and-myx VQASynth: https://github.com/remyxai/VQASynth
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