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cs.LG2026
MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents
Ruike Cao, Fanyu Zhao, Fugen Yao +6
The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over its response. Yet this capab…
cs.LG2026
Answer First, Reason Later: Aligning Search Relevance via Mode-Balanced Reinforcement Learning
Shijie Zhang, Xiang Guo, Rujun Guo +4
Building a search relevance model that achieves both low latency and high performance is a long-standing challenge in the search industry. To satisfy the millisecond-level response…
cs.LG2026
ETR: Outcome-Guided Elastic Trust Regions for Policy Optimization
Shijie Zhang, Kevin Zhang, Zheyuan Gu +5
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an important paradigm for unlocking reasoning capabilities in large language models, exemplified by the success…