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Reinforcement learning with verifiable rewards improves reasoning, while the allocation of learning signal shapes which solutions remain accessible under repeated sampling. Group-relative objectives assign equal advantages to equally rewarded responses, making aggregate credit proportional to sampled mode frequency. We...
This work aims to identify the benefits of HRL from the perspective of the fundamental challenges in decision-making, as well as highlight its impact on the performance trade-offs of AI agents.
This paper introduces a state connectivity model designed to predict pairwise state connectivity strength in asymmetric environments, and transforms these connectivity strengths into scalar auxiliary dense rewards, providing continuous guidance across multiple hierarchical levels.
Preliminary work toward optimizing plasticity within Markov decision processes is presented and it is shown that there exists a Bellman optimality equation for optimizing plasticity similar to previous work for empowerment.
This work proposes a fundamental reformulation of the RL objective by replacing the scalar reward with a distribution over reward functions, and applying a non-linear objective over sets of actions, and develops a framework in which calibrated behavioural diversity emerges naturally, remains controllable through the re...
This work reduces target variance and yields well-calibrated predictive uncertainty under stochastic observations in continuous control, providing a principled framework for planning with learned distribution models.
This work introduces the first benchmark suite and simulation framework tailored to pediatric SIC training, and proposes SIC-Agents, a self-improving framework that generates a clinician-editable skill document to guide simulator behavior.
This work formalizes CL as an online optimization problem governed by the interaction between environmental and learning dynamics, and introduces Transfer Efficiency as a quantitative measure of the tension between Instability, the bias inherited from conflicting past experience, and Transient Error, the optimization c...
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