Author
University of Illinois Urbana-Champaign
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This work proposes self-instructing policy optimization (SIPO) with a contrastive self-teacher to provide dense credit, and demonstrates that SIPO outperforms both RLVR and OPSD baselines without an external teacher or additional generation.
Topology-Aware Tokenization (TopoTok) is proposed, an item tokenization framework that preserves item relational structure throughout the quantization hierarchy and effectively alleviates topology distortion and consistently outperforms state-of-the-art tokenizers.
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