4 papers
Learning is Forgetting: LLM Training As Lossy Compression
Henry C. Conklin, Tom Hosking, Tan Yi-Chern +5
Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are structured. This limits our abilit…
Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations
Preethi Seshadri, Samuel Cahyawijaya, Ayomide Odumakinde +2
Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. T…
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users
Nishant Balepur, Matthew Shu, Yoo Yeon Sung +5
To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or e…
Scalpel vs. Hammer: GRPO Amplifies Existing Capabilities, SFT Replaces Them
Neel Rajani, Aryo Pradipta Gema, Seraphina Goldfarb-Tarrant +1
Training large language models (LLMs) for reasoning via maths and code datasets has become a major new focus in LLM post-training. Two particularly popular approaches are reinforce…