arXiv AI
Jul 9
RLVP: Penalize the Path, Reward the Outcome
arXiv:2607. 07435v1 Announce Type: cross Abstract: Agents acting on our behalf in the real world (e.
Training and Evaluating Ethical Reinforcement Learning Agents on Per-Episode Distributions
arXiv:2608. 14642v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents trained on a single reward signal exploit the gap between the designed reward and the intended behavior.
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arXiv AI
2d ago
Inducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent Evaluation
arXiv:2608. 13564v1 Announce Type: new Abstract: Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable environment reward, is expensive, slow, or unavailable at deployment time.
arXiv Machine Learning
1d ago
The Ethical Decision Head: Operationalizing Normative Ethics in Autonomous Vehicles via Reinforcement Learning from Human Feedback
arXiv:2608. 16710v1 Announce Type: new Abstract: As autonomous vehicles (AVs) approach Level 4 and Level 5 operational capability [SAE International, 2018], their on- board decision systems must handle not only safety-critical locomotion but also their subsequent moral weight.
arXiv AI
Jun 9
Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human
arXiv:2606. 08919v1 Announce Type: new Abstract: As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person.
arXiv Machine Learning
Jun 18
Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards
arXiv:2606. 18963v1 Announce Type: new Abstract: We study online reward-punishment learning when the environment provides no scalar reward or evaluative label.
arXiv Machine Learning
Jul 13
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
arXiv:2607. 08925v1 Announce Type: new Abstract: Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the goal is therefore to minimize falls during training rather than trade them off against return, as constrained Markov decision process (MDP) formulations do.
arXiv AI
Jul 14
Auditing the Risk Claims of Distributional Reinforcement Learning
arXiv:2607. 11607v1 Announce Type: new Abstract: Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring.
arXiv Machine Learning
Jun 25
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
arXiv:2606. 25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving.
arXiv AI
Jul 17
Reward-Free Evolving Agents via Pairwise Validator
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
arXiv AI
6d ago
Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL
arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.
arXiv Machine Learning
Aug 4
Training Small LLMs as Spatial Multi-Agent Policies
arXiv:2608. 01425v1 Announce Type: cross Abstract: Training LLM-based multi-agent systems with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that such systems should be judged by their behavior, not only their reward.
arXiv AI
Jul 21
Expected Free Energy as Belief-Dependent Utility for rho-POMDPs
arXiv:2607. 16981v1 Announce Type: new Abstract: An agent acting under partial observability must decide when to gather information and which observations are worth their cost.