arXiv Machine Learning
Jun 26
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
arXiv:2606. 26463v1 Announce Type: new Abstract: Deliberating takes time.
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Deliberating takes time. In real-time settings, that time is not free.
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arXiv AI
Jul 23
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
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arXiv AI
Jul 21
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
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arXiv AI
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ProSpec RL: Plan Ahead, then Execute
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Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning
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arXiv Machine Learning
Jun 16
On the Role of Computation in Reinforcement Learning
arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?
arXiv AI
Jun 3
Libra: Efficient Resource Management for Agentic RL Post-Training
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
arXiv AI
Jun 29
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
arXiv:2605. 30719v2 Announce Type: replace-cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.
arXiv AI
Jul 15
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.
arXiv AI
Jun 15
Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models
arXiv:2606. 14375v1 Announce Type: cross Abstract: Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules.
arXiv AI
Jun 9
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
arXiv:2606. 08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms.
arXiv AI
Jun 16
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
arXiv:2606. 16995v1 Announce Type: new Abstract: Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation.