BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Aligning Language Model Agents to Environment Dynamics X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2025-02-10-kolby-nottingham@cml.ics.uci.edu DTSTAMP:20250210T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20250210T130000 DTEND;TZID=America/Los_Angeles:20250210T140000 SUMMARY:[CML Seminar] Kolby Nottingham: Aligning Language Model Agents to E nvironment Dynamics LOCATION:Donald Bren Hall 4011 DESCRIPTION:Kolby Nottingham\, PhD Student\, Department of Computer Science \, University of California\, Irvine\n\nTitle: Aligning Language Model Age nts to Environment Dynamics\n\nAbstract: Language model agents are tacklin g challenging tasks from embodied planning to web navigation to programmin g. These models are a powerful artifact of natural language processing res earch that are being applied to interactive environments traditionally res erved for reinforcement learning. However\, many environments are not nati vely expressed in language\, resulting in poor alignment between language representations and true states and actions. Additionally\, while language models are generally capable\, their biases from pretraining can be unali gned with specific environment dynamics. In this talk\, I cover our resear ch into rectifying these issues through methods such as: (1) mapping high- level language model plans to low-level actions\, (2) optimizing language model agent inputs using reinforcement learning\, and (3) in-context polic y improvement for continual task adaptation.\n\nhttps://cml.ics.uci.edu/se minars/2025-02-10-kolby-nottingham X-ALT-DESC;FMTTYPE=text/html:Kolby Nottingham\, PhD Stud ent\, Department of Computer Science\, University of California\, Irvine
Title: Aligning Language Model Agents to Environment Dynamics

Abstract: Language model agents are tackling challenging ta sks from embodied planning to web navigation to programming. These models are a powerful artifact of natural language processing research that are b eing applied to interactive environments traditionally reserved for reinfo rcement learning. However\, many environments are not natively expressed i n language\, resulting in poor alignment between language representations and true states and actions. Additionally\, while language models are gene rally capable\, their biases from pretraining can be unaligned with specif ic environment dynamics. In this talk\, I cover our research into rectifyi ng these issues through methods such as: (1) mapping high-level language m odel plans to low-level actions\, (2) optimizing language model agent inpu ts using reinforcement learning\, and (3) in-context policy improvement fo r continual task adaptation.

https://cml.ics.uci.edu/seminars/2025-02- 10-kolby-nottingham URL:https://cml.ics.uci.edu/seminars/2025-02-10-kolby-nottingham END:VEVENT END:VCALENDAR