arXiv:2610.07406v1 Announce Type: new Abstract: Active feature acquisition learns policies that sequentially acquire features to maximize information about a target variable. We study how to learn an...
The paper introduces PICLE-PFN, a foundation model for longitudinal causal effect estimation that handles dynamic treatment policies. It represents policies via in-context history-decision demonstrations and is pretrained on synthetic data from longitudinal structural causal models, enabling it to predict conditional average potential outcomes in a single forward pass without task-specific training. Experiments on synthetic and semi-synthetic benchmarks show competitive RMSE and better uncertainty calibration while reducing deployment runtime compared to conventional estimators.
arXiv:2606.07399v3 Announce Type: replace-cross Abstract: Generative models are increasingly used to support decision-making in complex systems, where interventions may be joint and high-dimensional,...
The paper introduces PFN‑OPE, a prior‑data fitted network that performs amortized off‑policy evaluation (OPE) for large language models (LLMs). Unlike traditional OPE methods that must be retrained for each new logged dataset or reward definition, PFN‑OPE is pretrained once on a diverse set of contextual‑bandit tasks and can estimate value for any new logged dataset and target response in a single forward pass. Experiments on HelpSteer2 and UltraFeedback with Qwen, Llama, and Gemma policies show that PFN‑OPE reduces error by 2.0 to 9.3 times compared to the best baselines in reward‑shifted settings.
arXiv:2607. 11720v1 Announce Type: cross Abstract: Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction.
The paper investigates how non‑monotonic performance dynamics in single‑task reinforcement learning affect policy selection. By introducing post‑hoc diagnostic metrics, the authors evaluate common selection strategies on continuous‑control benchmarks and find that typical practices often miss the best policy, leading to suboptimal performance and wasted computation. They propose a validation‑based selection method that better approximates the best visited policy while improving efficiency.
arXiv:2609.13730v1 Announce Type: new Abstract: Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work h...
arXiv:2507. 20068v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) methods estimate the value of a new reinforcement learning (RL) policy prior to deployment.
The paper introduces Bidirectional Behavior Prior Distillation (B2PD), a method that uses action‑value priors to train a conditional variational autoencoder for generating high‑value behavior support. These expert behavior priors are then distilled into the online reinforcement learning agent, reducing inefficient exploration and stabilizing policy updates. Experiments on state‑ and pixel‑based tasks show that B2PD improves sample efficiency while maintaining stable learning dynamics.
arXiv:2603.18773v2 Announce Type: replace-cross Abstract: LLM post-training pipelines that combine supervised fine-tuning and reinforcement learning are difficult to configure under realistic compute...
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
arXiv:2406.03894v2 Announce Type: replace Abstract: Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due...