arXiv AI
Aug 7
ProDVI: Programmatic Dynamics Priors for Value Network Initialization
arXiv:2608. 06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient.
ProDVI: Programmatic Dynamics Priors for Value Network Initialization
Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch, forcing them to acquire task-relevant knowledge through online interaction.
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arXiv AI
Jul 16
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3
arXiv:2607. 01531v2 Announce Type: replace Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
arXiv Machine Learning
Jun 17
EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning
arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.
arXiv AI
Jul 3
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
arXiv:2607. 01531v1 Announce Type: new Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks.
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Jul 1
OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution.
arXiv Machine Learning
Jul 16
Pretraining in Actor-Critic Reinforcement Learning for Locomotion
arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.
arXiv AI
Jun 2
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
arXiv:2606. 00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics.
arXiv Machine Learning
Jun 25
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
arXiv:2501. 14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).
arXiv AI
Jul 23
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
arXiv AI
Jun 17
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.
arXiv Machine Learning
Jun 5
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.
arXiv Machine Learning
Jul 21
Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR
arXiv:2509. 02522v3 Announce Type: replace-cross Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches.