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cs.CL2026

Teaching a Moving Student: Rethinking the Curriculum of On-Policy Distillation

Lingxiang Hu, Tianle Xia, Ming Xu +6

In on-policy distillation (OPD), the student determines which states receive teacher supervision. As its policy evolves, earlier response prefixes become less likely even though te…

cs.CL2026

Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning

Leichao Dong, Dongxu Zhang, Yiding Sun +4

Large reasoning models often solve problems through long chain-of-thought (CoT) traces, yet much of this computation is spent on redundant derivations, repeated self-verification,…

cs.CL2026

GraSP: Graph-Structured Skill Compositions for LLM Agents

Tianle Xia, Lingxiang Hu, Yiding Sun +5

Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve performance -- focused sets o…

cs.CL2026

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

Wenwei Li, Ming Xu, Tianle Xia +7

Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations,…

cs.CL2026

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training

Tianle Xia, Ming Xu, Lingxiang Hu +7

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-…

cs.CL2026

AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Lingxiang Hu, Yiding Sun, Tianle Xia +6

While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains an open problem. Existing benchmarks…