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cs.LG2026
TAP: Efficient Long-Horizon Agent Pruning via Trajectory-Anchored Recovery
Yuanzhe Li, Pengxin Wang, Yuxin Ren +5
Emerging long-horizon agentic tasks require repeated model calls, worsening the inference cost of already-costly language models. While narrow agentic tasks suggest potential for a…
cs.LG2026
Learning Perturbation Robust Policies for LLM Agents with Stable Optimization
Pengxin Wang, Yuanzhe LI, Yuxin Ren +2
Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policies can be s…
cs.LG2026
From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation
Yuxin Ren, Maxwell D Collins, Miao Hu +1
Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensive. Replacing attention with si…