Shyam Sunder Kumar

theainerd

AI & ML interests

Natural Language Processing

Recent Activity

liked a model 3 days ago
deepseek-ai/DeepSeek-R1-0528
upvoted a collection 3 days ago
MobileLLM
reacted to codelion's post with ๐Ÿš€ 4 days ago
๐Ÿง  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. ๐Ÿš€ 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. ๐Ÿ“Š Results across math benchmarks: Arena Hard: 29% โ†’ 37.6% (+8.6%) AIME24: 23.33% โ†’ 30% (+6.67%) OptILLMBench: 61% โ†’ 65% (+4%) The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently. โœจ Key benefits: ๐Ÿ”„ Cumulative learning over time ๐Ÿ“– Transparent, inspectable strategies ๐Ÿ”Œ Works with any OpenAI-compatible API โšก 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. ๐Ÿ”— GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl ๐Ÿ“– Full article: https://huggingface.co/blog/codelion/system-prompt-learning ๐Ÿฆ 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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