HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
A synthesis method for piecewise quadratic terminal costs and configuration-constrained polytopic terminal regions in linear MPC that match the infinite-horizon LQR cost near the origin.
citing papers explorer
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
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On Piecewise Quadratic Terminal Costs for MPC
A synthesis method for piecewise quadratic terminal costs and configuration-constrained polytopic terminal regions in linear MPC that match the infinite-horizon LQR cost near the origin.