1 citations · 1 across the 4 of their papers we have counts for
6 papers
ROSS: Relearning from Self-Generated Rollouts through Selective Supervision
Zhiwei Zhang, Huayu Deng, Fei Zhao +4
Large language model post-training generates self-generated rollouts through reinforcement learning and on-policy distillation, yet this experience is often treated as stale once t…
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors
Zhiwei Zhang, Fei Zhao, Rui Wang +6
Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: after a tool-call error, smaller models often fall into repetitive invalid…
Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
Qinglin Zhu, Yizhen Yao, Runcong Zhao +7
Autoregressive (AR) models remain the standard for natural language generation but still suffer from high latency due to strictly sequential decoding. Recent diffusion-inspired app…
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
Runcong Zhao, Qinglin Zhu, Hainiu Xu +3
Understanding character relationships is essential for interpreting complex narratives and conducting socially grounded AI research. However, manual annotation is time-consuming an…
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Xiaoshuai Song, Muxi Diao, Guanting Dong +13
Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on b…
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks
Andrea Sottana, Bin Liang, Kai Zou +1
Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the…