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提出了一种模型无关的连续时间强化学习框架 PhiBE-PI，仅用单个状态-动作评论家即可从离散时间离策略数据中学习策略，并在局部建立端到端次优性保证，同时揭示了时间离散化中的偏差-方差权衡。
This paper introduces TailCert, a PAC-Bayes Bernstein-based procedure for certifying upper tail quantiles of policies selected from logged contextual-bandit data, and derives matched finite-class certification boundaries showing how sample requirements scale with overlap factor, policy-search complexity, and safety margin.
论文提出 Gibbs-Gap Policy Optimization（GGPO），一种针对可验证奖励的离线/异步 RL 方法，通过最小化 KL 调整奖励的 log-mean-exp 对比来避免高方差重要性权重，并在数学推理基准上优于 GRPO 和 AGRO。
This paper proposes a rollout-based training framework (Anchor-Label Relabelling and In-Rollout Anchors) to recover full supervision for speculative decoding block drafters trained on off-policy corpora, boosting greedy accepted length by up to 36.5% over DFlash without modifying the training text.
The paper introduces Regularized Emphatic Temporal-Difference Learning (RETD), which ensures stability under constant stepsizes by normalizing the emphatic TD signal, with proofs of convergence and experimental validation.
Dream-RSI is a framework for scalable recursive self-improvement in AI agents that uses historical discovery trees to create a replay simulator for offline policy evaluation, reducing online costs and improving discovery efficiency.
This paper proposes environment evolution to incrementally increase task difficulty for terminal agents off-policy, enhancing continuous learning signals and improving performance on benchmarks like Terminal-Bench 2.1.
OpenAI presents Hindsight Experience Replay (HER), a reinforcement learning technique that enables robots to learn from failed attempts by retroactively treating achieved alternative outcomes as successful goals, allowing learning even with sparse reward signals.