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🧠 AI⚪ NeutralImportance 7/10
Learning to Answer from Correct Demonstrations
arXiv – CS AI|Nirmit Joshi, Gene Li, Siddharth Bhandari, Shiva Prasad Kasiviswanathan, Cong Ma, Nathan Srebro||7 views
🤖AI Summary
Researchers propose a new approach for training AI models to generate correct answers from demonstrations, using imitation learning in contextual bandits rather than traditional supervised fine-tuning. The method achieves better sample complexity and works with weaker assumptions about the underlying reward model compared to existing likelihood-maximization approaches.
Key Takeaways
- →New imitation learning framework outperforms traditional supervised fine-tuning for multi-correct-answer scenarios
- →Method requires only bounded-complexity reward models rather than bounded-complexity policy classes, a weaker assumption
- →Achieves logarithmic sample complexity in reward class cardinality with optimistic convergence rates
- →Approach works with arbitrarily adaptive demonstrations and handles single-step contextual bandit problems
- →Research addresses fundamental challenges in AI training where multiple correct answers exist for given prompts
#machine-learning#imitation-learning#contextual-bandits#supervised-fine-tuning#ai-training#reward-models#sample-complexity#arxiv
Read Original →via arXiv – CS AI
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