1 citations · 2 across the 4 of their papers we have counts for
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Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
Youngrok Park, Sangmin Bae, Hojung Jung +6
Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This is particularly important for successive model generations and m…
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Yunseon Choi, Sangmin Bae, Seonghyun Ban +6
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning reg…
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
Haanvid Lee, Jongmin Lee, Yunseon Choi +4
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by pra…