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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.
LEEPS is introduced, a Latent-Guided Explore--Exploit Prompt Sampler that adaptively balances the reuse of previously observed informative prompts with continued exploration of uncertain ones, thereby making exploration more targeted without additional rollouts.
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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