arXiv:2603.27765v1 Announce Type: new Abstract: Recommendation ranking is fundamentally an influence allocation problem: a sorting formula distributes ranking influence among
arXiv:2605.11151v1 Announce Type: new Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online
arXiv:2610.00061v1 Announce Type: new Abstract: Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than
arXiv:2610.24229v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for training reasoning models, but its
arXiv:2608.01050v1 Announce Type: new Abstract: Production LLM agents that select from large skill libraries face a limitation that semantic relevance alone cannot resolve: a
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