Policy Split
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Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
arXiv:2604.11510v2 Announce Type: replace Abstract: To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifurcates the policy into normal and high-entropy modes with a high-entropy prompt. While sharing model parameters, the two modes undergo collaborative dual-mode entropy regularization tailored to distinct objectives. Specifically, the normal mode optimizes for task...
US court upholds injunction against Trump policy banning transgender troops
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Conformal Risk Sharing: Certified Cost Allocation with Participation Guarantees
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H1B visa: US judge strikes down Trump’s $100,000 visa fee
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Andy Burham pledges to slash taxes on pubs if he become prime minister
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A Unified Framework for Locality in Scalable MARL
arXiv:2602.16966v2 Announce Type: replace Abstract: Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-local, meaning a perturbation at one agent affects the long-run value at another agent weakly when the two are far apart. In the average-reward setting, the standard way to certify locality is the Dobrushin row-sum bound on a single matrix $C^\pi$ that captures how...
Toward Operationalizing Rasmussen: Drift Observability on the Simplex for Evolving Systems
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Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
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Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark
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