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20232026
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7 papers · 1 filter

cs.RO2026

Post-Training, Finetuning, and Steering:A Field Guide for Curating Training Data

Maria Attarian, Yifan Ruan, Claas Voelcker +5

While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performan…

cs.LG2026

RH-Detect: A Unified Benchmark for Reward Hacking Detection

Junwei Quan, Evgenii Opryshko, Rohan Subramani +1

Reward hacking, where a model exploits an evaluation signal without completing the intended task, threatens the reliability of deployed language model systems. Existing datasets us…

cs.LG2026

World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning

Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart +1

Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching algorithms, yet no single algorithm performs best across environments, goals, and…

cs.LG2026

Modification-Considering Value Learning for Reward Hacking Mitigation in RL

Evgenii Opryshko, Umangi Jain, Igor Gilitschenski

Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a failure mode known as reward hacki…

cs.LG2026

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

Yifan Ruan, Chenyang Cao, Andreas Burger +7

Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy…

cs.CV2026

Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players

Fangfu Liu, Kai He, Tianchang Shen +7

World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many gen…

cs.RO2026

Dynamics Distillation for Efficient and Transferable Control Learning

Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2

Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulato…