In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not reveal how contextual information controls their predictions. We study the acquisition and trainability of ICL with a controlled diagnostic that separates copying a near...
Minglai Yang, Xin-Yu Guo, Ren-Xiong Wang et al.· 0 citations
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace. A judge may overlook this evidence or accept unsupported claims, including an agent's own reports of success. When rubric v...
Reza Sanayei, Razvan-Gabriel Dumitru, Sun-Jie Hou et al.· 0 citations
This work proposes Rubric Dropout, a one-line fix borrowed from neuron dropout that randomly drops a subset of the rubric's criteria before computing the reward, so the policy never optimizes the same rubric twice.
Minglai Yang, Xinyu Guo, Utkarsh Tyagi et al.· 0 citations
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