4 papers · 1 filter
Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis
Chain-of-thought (CoT) monitoring is a safety strategy where the reasoning of a large language model "actor" is inspected by a "monitor" (often another language model) for signs of…
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generat…
Pigeonholing: how bad prompts hurt models, causing collapse and mistakes
Hyunji Nam, Keertana Chidambaram, Dorottya Demszky +1
While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we call…
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existi…