5 citations · 5 across the 10 of their papers we have counts for
11 papers
On the Geometry of On-Policy Distillation
Zhennan Shen, Yanshu Li, Qingyu Yin +6
On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood. We characterize the trajectory of O…
Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineering
Chak Tou Leong, Dingwei Chen, Heming Xia +4
Large reasoning models (LRMs) have achieved remarkable success in complex problem-solving, yet they often suffer from computational redundancy or reasoning unfaithfulness. Current…
Probing the Difficulty Perception Mechanism of Large Language Models
Sunbowen Lee, Qingyu Yin, Chak Tou Leong +5
Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an es…
PoolingVQ: A VQVAE Variant for Reducing Audio Redundancy and Boosting Multi-Modal Fusion in Music Emotion Analysis
Dinghao Zou, Yicheng Gong, Xiaokang Li +2
Multimodal music emotion analysis leverages both audio and MIDI modalities to enhance performance. While mainstream approaches focus on complex feature extraction networks, we prop…
Emoanti: audio anti-deepfake with refined emotion-guided representations
Xiaokang Li, Yicheng Gong, Dinghao Zou +2
Audio deepfake is so sophisticated that the lack of effective detection methods is fatal. While most detection systems primarily rely on low-level acoustic features or pretrained s…
M-MRE: Extending the Mutual Reinforcement Effect to Multimodal Information Extraction
Chengguang Gan, Zhixi Cai, Yanbin Wei +3
Mutual Reinforcement Effect (MRE) is an emerging subfield at the intersection of information extraction and model interpretability. MRE aims to leverage the mutual understanding be…
Counterfactual experience augmented off-policy reinforcement learning
Sunbowen Lee, Yicheng Gong, Chao Deng
Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enha…
Quantification of Large Language Model Distillation
Sunbowen Lee, Junting Zhou, Chang Ao +11
Model distillation is a fundamental technique in building large language models (LLMs), transferring knowledge from a teacher model to a student model. However, distillation can le…
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking
Sunbowen Lee, Shiwen Ni, Chi Wei +7
Safety alignment mechanism are essential for preventing large language models (LLMs) from generating harmful information or unethical content. However, cleverly crafted prompts can…
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
Sunbowen Lee, Hongqin Lyu, Yicheng Gong +2
Reinforcement learning methods have proposed promising traffic signal control policy that can be trained on large road networks. Current SOTA methods model road networks as topolog…
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction
Chengguang Gan, Sunbowen Lee, Qingyu Yin +9
The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modele…