arXiv:2610.02828v1 Announce Type: new Abstract: Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL r...
arXiv:2608. 09586v1 Announce Type: new Abstract: The Independent Chip Model (ICM) converts tournament chips into reference prize equity, and policies are routinely constructed against those values.
RSD-Poker is a certification framework for residual policies in imperfect‑information games that adapts to game structure and remains robust to shifts in deployment state mixtures. It freezes a bank of residual families and a policy‑visible partition, then assigns each candidate‑group pair weighted upper and lower certificates for risk and utility. The framework guarantees that, with high probability, the selected candidate map meets its declared risk budget and utility bounds across a predeclared uncertainty set of deployment group proportions.
The paper investigates the problem of sharing a single critic across multiple parallel environments in reinforcement learning. It shows that when environments assign different expected returns to the same state, a shared critic must reconcile conflicting value targets, which can distort advantage estimates and misguide policy updates. The authors propose a simple fix—providing the critic with the environment index—demonstrating through bandit models and experiments on CartPole, MuJoCo, BipedalWalker, and 16 Procgen games that this conditional critic stabilizes learning and boosts returns, achieving a 40.8% improvement in aggregate normalized return on unseen levels.
arXiv:2609. 10954v1 Announce Type: new Abstract: Continual world models must decide whether new data justify changing the model.
arXiv:2607. 16895v1 Announce Type: new Abstract: Safe adaptive control is online adaptation under a safety guarantee on the learning trajectory itself.
BCPPO is a new variant of Proximal Policy Optimization that uses Bachelier-inspired cost‑prediction networks to generate a smooth penalty based on disagreement among critics. The method keeps temporal‑difference learning unchanged, applies a saturation‑aware controller to manage cost penalties, and deploys only the policy network. Across extensive experiments, BCPPO outperforms comparators in achieving higher mean returns while maintaining lower or comparable CVaR in all tested tasks.
The paper investigates how a reference policy can be used to steer regularized self‑play toward a specific equilibrium in two‑player zero‑sum games. By anchoring the reference at a target equilibrium and refining the self‑play process, the authors achieve precise convergence to that target with very low exploitability and coordinate error. The study also explores the effects of off‑manifold references, mirror‑step sizing, and boundary saturation on selection accuracy.
arXiv:2607. 11920v1 Announce Type: cross Abstract: Evaluating decisions made under uncertainty is hard when labeled outcomes are scarce, costly, or confounded with luck.
arXiv:2608.14761v2 Announce Type: replace-cross Abstract: At a finite public-chance cut, partial counterfactual regret minimization (CFR) can evaluate successive outcome batches at learner states cha...
arXiv:2608. 17841v1 Announce Type: cross Abstract: Multi-armed bandit algorithms are evaluated by regret, yet comparable regret can coexist with different allocations across independent runs.
arXiv:2610. 10250v1 Announce Type: new Abstract: We study minimum-variance control of an unknown autoregressive system with exogenous inputs and coefficients that change at unknown times.