1 citations · 1 across the 13 of their papers we have counts for
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Is Better Teacher Supervision Enough? Unlocking Student-side Learning in Multimodal On-Policy Distillation
Siyuan Liu, Kanghui Tian, Yue Duan +4
On-policy distillation (OPD) improves reasoning by providing token-level supervision from a teacher on a student's own trajectories. Existing methods primarily focus on enhancing t…
Learning Through Game: Skewed Transfer of Tabular Knowledge to Strengthen Image Model
Longfei Huang, Shangdong Yang, Yang Yang
Multimodal tabular-image learning is gaining growing attention, yet it faces challenges due to tabular data unavailable at test time. A practical solution involves transferring tab…
Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction
Xingguo Chen, Zhiang He, Yuchen Shen +4
Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDR…
Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction
Xingguo Chen, Yuchen Shen, Shangdong Yang +3
Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry i…
Regularized Centered Emphatic Temporal Difference Learning
Xingguo Chen, Chaohui Wu, Jinguo Ye +5
Off-policy temporal-difference (TD) learning with function approximation faces a structural tradeoff among stability, projection geometry, and variance control. Emphatic TD (ETD) i…
Bitboard version of Tetris AI
Xingguo Chen, Pingshou Xiong, Zhenyu Luo +6
The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Te…
OpenGuanDan: A Large-Scale Imperfect Information Game Benchmark
Chao Li, Shangdong Yang, Chiheng Zhan +5
The advancement of data-driven artificial intelligence (AI), particularly machine learning, heavily depends on large-scale benchmarks. Despite remarkable progress across domains ra…