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Extreme Region Policy Distillation

arXiv:2605.25582v2 Announce Type: replace-cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-policy methods discard trajectories after a single update, while off-policy reuse introduces distribution mismatch that existing trust-region techniques…

A Motivational Architecture for Conversational AGI

arXiv:2606.05411v1 Announce Type: new Abstract: Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs. Conversational agents operate in a different regime: their sensorimotor loop is linguistic, their environment is a user’s evolving mental state,…

OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation

arXiv:2606.06096v1 Announce Type: cross Abstract: Policy-gradient methods usually optimize expected return, but many real world applications care about distributional properties of returns: tail risk, outlier robustness, or best-of-K discovery. We introduce OrderGrad, a family of likelihood-ratio and reparameterization gradient estimators…

OPRD: On-Policy Representation Distillation

arXiv:2606.06021v1 Announce Type: cross Abstract: On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two limits: (1) sampling variance from Monte Carlo KL estimates over large vocabularies (e.g., Qwen’s ~150k tokens)…

Beyond Rewards in Reinforcement Learning for Cyber Defence

arXiv:2602.04809v3 Announce Type: replace-cross Abstract: Recent years have seen an explosion of interest in autonomous cyber defence agents trained to defend computer networks using deep reinforcement learning. These agents are typically trained in cyber gym environments using dense, highly engineered…