The paper introduces Frictive Policy Optimization as a risk-sensitive epistemic control framework for LLM alignment that treats interventions like clarification, verification, and refusal as explicit actions to improve downstream belief quality rather than immediate rewards.
InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 15503–15514
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Proposes OPAC for trajectory-level offline RL achieving 𝓣O(H^{2}√(C_sa(π*)/n)) bounds with matching lower bound, plus conditions for tractability in generalized nonlinear outcome settings.
State augmentation converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.
citing papers explorer
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Frictive Policy Optimization for LLMs: Epistemic Intervention, Risk-Sensitive Control, and Reflective Alignment
The paper introduces Frictive Policy Optimization as a risk-sensitive epistemic control framework for LLM alignment that treats interventions like clarification, verification, and refusal as explicit actions to improve downstream belief quality rather than immediate rewards.
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When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?
Proposes OPAC for trajectory-level offline RL achieving 𝓣O(H^{2}√(C_sa(π*)/n)) bounds with matching lower bound, plus conditions for tractability in generalized nonlinear outcome settings.
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Sample Complexity for Markov Decision Processes and Stochastic Optimal Control with Static Risk Measures
State augmentation converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.