BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UC Irvine//CML Seminars//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Reasoning in the Wild
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:2025-03-18-wenting-zhao@cml.ics.uci.edu
DTSTAMP:20250318T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250318T110000
DTEND;TZID=America/Los_Angeles:20250318T120000
SUMMARY:[CML Seminar] Wenting Zhao: Reasoning in the Wild
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Wenting Zhao\, PhD Student\, Department of Computer Science\, C
ornell University\n\nTitle: Reasoning in the Wild\n\nAbstract: In this tal
k\, I will discuss how to build natural language processing (NLP) systems
that solve real-world problems requiring complex reasoning. I will address
three key challenges. First\, because real-world reasoning tasks often di
ffer from the data used in pretraining\, I will introduce WildChat\, a dat
aset of reasoning questions collected from users\, and demonstrate how tra
ining on it enhances language models' reasoning abilities. Second\, becaus
e supervision is often limited in practice\, I will describe my approach t
o enabling models to perform multi-hop reasoning without direct supervisio
n. Finally\, since many real-world applications demand reasoning beyond na
tural language\, I will introduce a language agent capable of acting on ex
ternal feedback. I will conclude by outlining a vision for training the ne
xt generation of AI reasoning models.\n\nhttps://cml.ics.uci.edu/seminars/
2025-03-18-wenting-zhao
X-ALT-DESC;FMTTYPE=text/html:Wenting Zhao\, PhD Student\
, Department of Computer Science\, Cornell University
Title:
Reasoning in the Wild
Abstract: In this talk\, I will discu
ss how to build natural language processing (NLP) systems that solve real-
world problems requiring complex reasoning. I will address three key chall
enges. First\, because real-world reasoning tasks often differ from the da
ta used in pretraining\, I will introduce WildChat\, a dataset of reasonin
g questions collected from users\, and demonstrate how training on it enha
nces language models' reasoning abilities. Second\, because supervision is
often limited in practice\, I will describe my approach to enabling model
s to perform multi-hop reasoning without direct supervision. Finally\, sin
ce many real-world applications demand reasoning beyond natural language\,
I will introduce a language agent capable of acting on external feedback.
I will conclude by outlining a vision for training the next generation of
AI reasoning models.
https://cml.ics.uci.edu/seminars/2025-03-18-wenting-
zhao
URL:https://cml.ics.uci.edu/seminars/2025-03-18-wenting-zhao
END:VEVENT
END:VCALENDAR