6 citations · 6 across the 13 of their papers we have counts for
7 papers · 1 filter
Unlocking Token Rewards via Training-Free Reward Attribution
Sitong Wu, Haoru Tan, Bin Xia +6
In this paper, we propose an extremely efficient, training-free method to extract token-level reward signals directly from an existing deep reward model. Our core idea is to attrib…
Dynamic Important Example Mining for Reinforcement Finetuning
Haoru Tan, Sitong Wu, Yanfeng Chen +9
Reinforcement fine-tuning (RFT) is increasingly used to strengthen the reasoning abilities of large models, yet its effectiveness is bound by how training data are selected and use…
StreamArena: Toward Continuous, Interactive, and Long-Horizon Agentic Streaming Video Understanding
Xichen Zhang, Guankai Li, Yinghao Zhu +6
Deploying autonomous multimodal agents in continuous, real-world environments requires them to ingest unbounded audio-visual streams and maintain hour-scale memory. However, curren…
Dataset Distillation by Influence Matching
Haoru Tan, Wang Wang, Sitong Wu +5
We revisit dataset distillation from an outcome-centric perspective. Rather than aligning process surrogates (per-step gradients or training trajectories), Influence Matching (Inf-…
FastMix: Fast Data Mixture Optimization via Gradient Descent
Haoru Tan, Sitong Wu, Yanfeng Chen +5
While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture for pre-training and post-training remains a significant open pro…
Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention
Wenhu Zhang, Yiming Wu, Huanyu Wang +6
Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoregressive LLMs, while scaling…
SearchGym: Bootstrapping Real-World Search Agents via Cost-Effective and High-Fidelity Environment Simulation
Xichen Zhang, Ziyi He, Yinghao Zhu +6
Search agents have emerged as a pivotal paradigm for solving open-ended, knowledge-intensive reasoning tasks. However, training these agents via Reinforcement Learning (RL) faces a…