HiRE: Hindsight Reward Editing for Policy Finetuning
cs.RO
Submitted: 2026-09-22
Updated: 2026-09-22
Terminology
Sources
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- RoboReward: General-Purpose Vision-Language Reward Models for Robotics
- TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics
- Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
- VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
- SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
- From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning
- Rewarding DINO: Predicting Dense Rewards with Vision Foundation Models
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
- Proximal Policy Optimization Algorithms
- Residual Off-Policy RL for Finetuning Behavior Cloning Policies
- robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
- Efficient Online Reinforcement Learning with Offline Data
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