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X-WR-CALNAME:Reinforcement Learning With Constraints: From Theory to Reason
 ing in LLM - Lin Yang\, Assistant Professor\, UCLA
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TZID:America/Vancouver
TZUNTIL:20270314T100000Z
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DTSTART:20241103T020000
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TZOFFSETTO:-0800
RDATE:20251102T020000
RDATE:20261101T020000
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DTSTAMP:20260122T011248Z
CLASS:PUBLIC
CREATED:20250710T211821Z
DESCRIPTION:Abstract: In this talk\, I will explore reinforcement learning 
 with constraints\, focusing on both theoretical foundations and practical 
 applications. I will first present recent advances in the sample complexit
 y of constrained Markov decision processes (CMDPs)\, covering both offline
  and online settings. Our results establish near-optimal upper and lower b
 ounds under relaxed and strict feasibility regimes\, revealing that constr
 aint satisfaction—while generally harder—can match the sample efficiency o
 f unconstrained MDPs under certain conditions. These insights are grounded
  in primal-dual…
DTSTART;TZID=America/Vancouver:20250715T144500
DTEND;TZID=America/Vancouver:20250715T154500
LAST-MODIFIED:20250715T152032Z
LOCATION:UBC Vancouver Campus\, Fried Kaiser (KAIS) building\, Room 2020/20
 30\, 2332 Main Mall
SUMMARY:Reinforcement Learning With Constraints: From Theory to Reasoning i
 n LLM - Lin Yang\, Assistant Professor\, UCLA
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/reinforcement-learning-constraints-theory-re
 asoning-llm-lin-yang-assistant-professor-ucla
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