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UID:2025-10-24-furong-huang@cail.columbia.edu
DTSTAMP:20260823T060954Z
DTSTART:20251024T150000Z
DTEND:20251024T160000Z
SUMMARY:ML Seminar: Furong Huang - Rethinking Test-Time Thinking: From Tok
 en-Level Rewards to Robust Generative Agents
LOCATION:School of Social Work\, Room C03
DESCRIPTION:We present a unified perspective on test-time thinking as a le
 ns for improving generative AI agents through finer-grained reward modelin
 g\, data-centric reasoning\, and robust alignment. Beginning with GenARM\,
  we introduce an inductive bias for denser\, token-level reward modeling t
 hat guides generation during decoding\, enabling token-level alignment wit
 hout retraining. While GenARM targets reward design\, ThinkLite-VL focuses
  on the data side of reasoning. It proposes a self-improvement framework t
 hat selects the most informative samples via MCTS-guided search\, yielding
  stronger visual reasoning with fewer labels. Taking this a step further\,
  MORSE-500 moves beyond selection to creation: it programmatically generat
 es targeted\, controllable multimodal data to systematically probe and str
 ess-test models' reasoning abilities. We then interrogate a central assump
 tion in inference-time alignment: Does Thinking More Always Help? Our find
 ings reveal that increased reasoning steps can degrade performance–not d
 ue to better or worse reasoning per se\, but due to rising variance in out
 puts\, challenging the naive scaling paradigm. Finally\, AegisLLM applies 
 test-time thinking in the service of security\, using an agentic\, multi-p
 erspective framework to defend against jailbreaks\, prompt injections\, an
 d unlearning attacks–all at inference time. Together\, these works chart
  a path toward generative agents that are not only more capable\, but more
  data-efficient\, introspective\, and robust in real-world deployment.\n\n
 https://cail.columbia.edu/events/2025-10-24-furong-huang
URL:https://cail.columbia.edu/events/2025-10-24-furong-huang
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