{"682874":{"#nid":"682874","#data":{"type":"event","title":"PhD Defense by Yuchen Zhuang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Advancing Reasoning and Planning in Large Language Models via Reward Shaping\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EJuly 1st, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;\u003C\/strong\u003E4:30\u0026nbsp;- 6:00 PM EST\u003C\/p\u003E\u003Cp\u003ELocation: Online\u003C\/p\u003E\u003Cp\u003EZoom link:\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/99388025469\u0022 title=\u0022https:\/\/gatech.zoom.us\/j\/99388025469\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/99388025469\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EYuchen Zhuang\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003ESchool of Computer Science and Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E1 Dr. Chao Zhang (CSE, Georgia Tech, Advisor)\u003C\/p\u003E\u003Cp\u003E2 Dr. Bo Dai (CSE, Georgia Tech, Google DeepMind)\u003C\/p\u003E\u003Cp\u003E3 Dr. Tuo Zhao (ISYE, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E4 Dr. Steve Mussmann (CS, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E5 Dr. Sherry Yang (NYU, Google DeepMind)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ERecent advancements in large language models (LLMs) have significantly enhanced their reasoning and planning capabilities, enabling them to serve effectively in complex, real-world scenarios. Despite these improvements, achieving human-level performance remains challenging, particularly for tasks requiring extensive multi-step reasoning and sophisticated planning. Motivated by these limitations, my dissertation focuses on improving the reasoning and planning abilities of LLMs through reward shaping to guide LLM decision-making by optimizing rewards for desired outcomes.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe\u0026nbsp;core contributions of this thesis are organized around three key aspects of effective and robust reasoning in LLM agents: (1) Formulating and Evaluating LLM-based Agents for External Tool Use. Effectively leveraging external tools is crucial for extending the practical utility of LLMs.\u0026nbsp;(2) Efficient Action Space Navigation in LLM Agents. The complexity of multi-step planning tasks, involving numerous candidate actions, demands efficient exploration strategies. (3) Lightweight Adaptation for Black-Box LLM Personalization. The practical deployment of LLMs often involves adapting models to specific users without access to internal model parameters. Together, these thrusts represent a cohesive, data-centric strategy for enhancing LLM capabilities, systematically improving their ability to reason, plan, and adapt efficiently in complex, real-world environments.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAdvancing Reasoning and Planning in Large Language Models via Reward Shaping\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Advancing Reasoning and Planning in Large Language Models via Reward Shaping"}],"uid":"27707","created_gmt":"2025-06-24 17:13:13","changed_gmt":"2025-06-24 17:13:13","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-07-01T16:30:00-04:00","event_time_end":"2025-07-01T18:00:00-04:00","event_time_end_last":"2025-07-01T18:00:00-04:00","gmt_time_start":"2025-07-01 20:30:00","gmt_time_end":"2025-07-01 22:00:00","gmt_time_end_last":"2025-07-01 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Zoom link","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}