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A series of models are explored that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation, including OneReason.
Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities. We study this trade-off in Multi-Teacher On-Policy Distillation (MOPD), where a specialized model learns from domain and general teachers on its own sampled trajectories. Standard MOPD faces...
Results show that generative retrieval can combine shared modeling with objective-specific control and complementary candidate generation, and under the same 512-item retrieval budget, Multi-Decoder OneRec improves over the single-decoder OneRec baseline.
Counteraction-Aware Multi-Teacher On-Policy Distillation is proposed, which addresses issues with decoupled alternating training and gap-based sample selection and outperforms all baselines in general capability recovery while maintaining domain-specific behavior.
A consistency boundary analysis is presented that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain and proposes InfoMamba, an attention-free hybrid architecture that consistently outperforms strong Transformer and SSM baselines.
WhisperRec compresses teacher-generated CoT into learnable latent reasoning tokens, enabling a Latent-Reason-then-Answer paradigm that performs reasoning in latent space without producing verbose rationales, and achieves over 10x higher online inference throughput.
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