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seohyun8825

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upvoted a paper 2 days ago

GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents

upvoted a paper 2 days ago

SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis

reacted to codelion's post with 🔥 4 days ago

🧠 We just implemented Andrej Karpathy's "third paradigm" for LLM learning! System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts. 🚀 How it works: Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates. 📊 Results across math benchmarks: Arena Hard: 29% → 37.6% (+8.6%) AIME24: 23.33% → 30% (+6.67%) OptILLMBench: 61% → 65% (+4%) The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently. ✨ Key benefits: 🔄 Cumulative learning over time 📖 Transparent, inspectable strategies 🔌 Works with any OpenAI-compatible API ⚡ Simple integration: just add "spl-" prefix to your model Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them! This feels like a genuine step toward AI that learns from experience while staying completely interpretable. 🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl 📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning 🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486 Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?

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