arXiv AI
Jun 2
Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
arXiv:2606. 00680v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets.
Efficient Adaptive Data Acquisition via Pretrained Belief Representations
arXiv:2606. 25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.
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arXiv AI
2d ago
Finetuning with Sampling: SFT Learns Better Than You Think
arXiv:2610.02140v1 Announce Type: cross Abstract: Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and r...
arXiv Machine Learning
Jul 31
On-Policy and Off-Policy Learning for Large Action Spaces
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv AI
Jun 19
Can In-Context Learning Support Intrinsic Curiosity?
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
Hugging Face Trending Papers
Jun 17
Can In-Context Learning Support Intrinsic Curiosity?
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated data selection, or "intrinsic curiosity", remains a significant challenge.
arXiv AI
Jul 3
Full Bayesian Reinforcement Learning via LF-IBIS
arXiv:2607. 01741v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is a sequential decision-making framework in which an agent learns optimal policies through interaction with an environment by maximizing cumulative rewards.
arXiv Machine Learning
Sep 18
Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation
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 Machine Learning
Aug 11
Path-dependent Discrete Amortized Inference
arXiv:2608. 08644v1 Announce Type: new Abstract: We consider the problem of sampling compositional and discrete objects from a given unnormalized posterior distribution.
arXiv AI
Jul 1
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
arXiv:2606. 31184v1 Announce Type: cross Abstract: Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency.
arXiv Statistics ML
3d ago
Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
arXiv:2609.39525v1 Announce Type: new Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
Hugging Face Trending Papers
Aug 9
Path-dependent Discrete Amortized Inference
We consider the problem of sampling compositional and discrete objects from a given unnormalized posterior distribution. Notably, recent studies have shown that this problem can be efficiently solved by learning a deterministic Markov Decision Process (MDP) that progressively builds each object in proportion to the posterior.
arXiv Machine Learning
Aug 12
IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning
arXiv:2608. 10634v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making.