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rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 257 -
URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
Paper • 2501.04686 • Published • 50 -
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though
Paper • 2501.04682 • Published • 90 -
Agent Laboratory: Using LLM Agents as Research Assistants
Paper • 2501.04227 • Published • 85
Isaac Kargar
kargarisaac
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AI & ML interests
Interested in computer vision, nlp, and reinforcement learning
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AGENTS + FINETUNING! This week Hugging Face learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
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The Large Language Model Course
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Open R1: Update #2
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