7 papers
Efficient Reinforcement Learning for Long-Horizon Tool-Use Agentic Tasks
Zelei Cheng, Amritansh Mishra, Sambit Sahu +1
Long-horizon tool-using agents must reason over user goals, domain policies, tool calls, simulator state, and delayed verifiable rewards. Reinforcement learning (RL) is a natural f…
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking
Chia-Hsuan Lee, Sihui Dai, Mingyang Zhou +5
Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction that consume tokens without im…
SEAD: Competence-Aware On-Policy Distillation via Entropy-Guided Supervision
Chia-Hsuan Lee, Zelei Cheng, Yu Wang +4
On-policy distillation (OPD) has a property absent in offline distillation and RL: teacher supervision quality depends on student competence. Incoherent rollouts yield noisy gradie…
RECAP: Regression Evaluation for Continual Adaptation of Prompts
Harsh Deshpande, Kushal Chawla, Sangwoo Cho +2
Production agentic systems routinely face evolving constraints and must comply from the very next interaction. Scenarios like a tool-call notification changing a compliance thresho…
MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian +7
Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. However, existing memory systems often co…
Decomposing the Delta: What Do Models Actually Learn from Preference Pairs?
Chia-Hsuan Lee, Mingyang Zhou, Renkun Ni +6
Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of preference data drive downstream…