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Emiliano Penaloza

3 accepted papers

2026

Privileged Information Distillation for Language Models

ICML 2026poster

Training-time privileged information (PI) can enable language models to succeed on tasks they would otherwise fail, making it a powerful tool for reinforcement learning in hard, long-horizon settings. However, transferring capabilities learned with PI to policies that must act without it at inferenc…

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2025

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization

ICML 2025poster

Concept Bottleneck Models (CBMs) propose to enhance the trustworthiness of AI systems by constraining their decisions on a set of human understandable concepts. However, CBMs typically rely on datasets with assumedly accurate concept labels—an assumption often violated in practice which we show can…

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2025

How to Train Your LLM Web Agent: A Statistical Diagnosis

NeurIPS 2025poster

Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary agents. Bridging this gap is key to enabling customizable, efficient, and privacy-preserving agents. Two challenges hinder progress: the reproducibility issues in RL and L…

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