arXiv AI
Jun 18
AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
arXiv:2606. 18617v1 Announce Type: cross Abstract: There exist numerous tutor training platforms.
PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
arXiv:2603. 05361v2 Announce Type: replace Abstract: 9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances.
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arXiv AI
Jul 13
Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learned
arXiv:2602. 13241v3 Announce Type: replace-cross Abstract: Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a sustained training crisis: staffing shortages exceed 25\% in many centers, and preparing a single new hire can require up to 720 hours of one-on-one instruction that removes experienced personnel from active duty.
arXiv AI
Jul 24
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
arXiv:2607. 21419v1 Announce Type: new Abstract: In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization.
arXiv Machine Learning
Aug 6
Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning
arXiv:2608. 05080v1 Announce Type: new Abstract: Critic-free group-based reinforcement learning has become a scalable approach for post-training large language models.
arXiv AI
Aug 7
Search2Skill: Skill Distillation Beyond Knowledge Boundaries Via Rubric-Based Reinforcement Learning
arXiv:2608. 05245v1 Announce Type: new Abstract: Reusable skills, which encapsulate the procedural knowledge required to solve real-world professional tasks, offer LLM-based agents a path toward self-evolution in expert domains.
arXiv Machine Learning
Jul 30
SCOUT: Per-Context Reset Curricula for Sparse-Reward Reinforcement Learning
arXiv:2607. 26417v1 Announce Type: new Abstract: Sparse-reward reinforcement learning often fails because rollouts from the unassisted evaluation start rarely reach later task stages.
arXiv AI
Jun 16
Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call
arXiv:2606. 15225v1 Announce Type: cross Abstract: Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement.
arXiv Machine Learning
Jun 29
Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
arXiv AI
Aug 10
ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv AI
Jun 17
Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects
arXiv:2606. 17706v1 Announce Type: cross Abstract: Curriculum learning couples two design choices, how samples are scored by difficulty and how harder samples are paced into training, making it difficult to attribute observed gains to either component.
arXiv AI
1d ago
Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning
arXiv:2608. 16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations.
arXiv AI
Aug 7
Skill Neologisms: Towards Skill-based Continual Learning
arXiv:2605. 04970v3 Announce Type: replace-cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly.