The Statistical Benefits of Multiple Responses for Learning from Demonstrations

arXiv:2609.33291 · stat.ML, cs.IT, cs.LG, math.IT, math.ST, stat.TH · Submitted 2026-09-27 · Read on arXiv

stat.ML, cs.IT, cs.LG, math.IT, math.ST, stat.TH

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Sample Complexity — 14× in this paper · explained in 15 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Regret — 4× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• List Learning — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Best-of-N Sampling — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N Sampling via max@k Optimisation
• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
• Evaluating Large Language Models Trained on Code
• Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
• Learning with Multiple Correct Answers -- Regret Bounds under Different Feedback Models

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