Poster · Sydney

ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning

Name: ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
Start: 2026-12-09T10:00:00+11:00
End: 2026-12-09T13:00:00+11:00
Location: Hall 1-4, ICC Sydney

When: Wed 9 Dec 2026, 10:00–13:00 AEDT
Where: Hall 1-4, ICC Sydney
Exhibition Centre, Level 1
Session: Sydney Poster Session 3
Authors: Zhishen Sun, Sizhe Dang, Guang Dai, Haishan Ye
Organisations: Xi'an Jiaotong University, Sgit Ai, The Chinese University of Hong Kong
QRS classification: Reinforcement learning, Language models, Efficient and distributed ML systems

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