From the 1 of 18 linked papers with an AI index.
18 papers
Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD Shape CoT Calibration
Shuhao Li, Guodong Du, Anhao Zhao +3
The paper examines how supervised fine-tuning, reinforcement learning, and on‑policy distillation affect confidence estimates of large language models during chain‑of‑thought reaso…
miniReranker: Efficient Multimodal Reranking through Visual Cache Reuse and Interaction Sparsity
Yingqi Fan, Xuan Lu, Anhao Zhao +6
Multimodal large language models (MLLMs) have recently shown strong potential as point-wise rerankers by directly modeling query--document relevance through next-token prediction.…
PowerOPD: Stabilizing On-Policy Distillation with Bounded Power Transformation
Anhao Zhao, Junlong Tong, Yingqi Fan +3
Standard on-policy distillation (OPD) for large language models estimates the reverse-KL objective using student-sampled tokens, yielding an unbiased single-sample Monte Carlo esti…
AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization
Junlong Tong, Wenqi Xu, Yingqi Fan +4
Large reasoning models typically follow a read-then-think paradigm: they observe the complete input, reason over a static context, and then produce the answer. Yet many real-world…
Escaping the KL Agreement Trap in On-Policy Distillation
Haoran Xin, Anhao Zhao, Ying Sun +3
On-policy distillation (OPD) provides dense token-level supervision by asking a teacher to score student-generated rollouts. However, when the student drifts into an unrecoverable…
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Haozhe Hu, Hao Wu, Anhao Zhao +4
Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers…