High-order generator regression from multi-step trajectories yields a second-order accurate estimator for finite-horizon continuous-time policy evaluation that outperforms the Bellman baseline in calibration studies and benchmarks.
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REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation
High-order generator regression from multi-step trajectories yields a second-order accurate estimator for finite-horizon continuous-time policy evaluation that outperforms the Bellman baseline in calibration studies and benchmarks.
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Representation-Guided Parameter-Efficient LLM Unlearning
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.