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SPEE is a unified post-training framework that sequentially performs explicit experience evolution followed by implicit policy optimization, and demonstrates that SPEE consistently outperforms both test-time and training-time self-evolution baselines across three model scales.
SkillEval is used to evaluate skills in controlled quality tests and it is used for diagnosing weaknesses in skill documents and guiding targeted revisions, and it is shown that SkillEval reliably distinguishes skills of different quality.
This work shows that scenario-wrapped prompts activate internal scenario directions whose causal steering consistently reduces refusal scores, and proposes Concept2Scenario, a concept-based attribution framework for vulnerable scenario discovery that instantiates a broad concept space with a sparse autoencoder, transla...
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