From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
arXiv:2606.00083v1 Announce Type: new Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored the zero-shot reasoning capabilities of pre-trained Vision-Language Models (VLMs) as reward models. However, without careful prompt engineering, these approaches tend to produce suboptimal rewards, where false positive predictions can severely degrade downstream policy learning. In robotics, limited datasets comprising expert demonstrations are often collected to bootstrap policy learning. This scenario provides an opportunity to optimize a reward model prior policy training. We propose Demo2Reward a test-time adaptation technique to optimize the language instruction of a reward model based on a few demonstrations (3-10 trajectories) to reduce false positives while preserving true positives. Crucially, this requires no additional model training or computation resources during policy learning. We show that Demo2Reward consistently outperforms existing zero- and few-shot VLM reward models across a range of simulated robotic tasks and policy backbones. Finally, we demonstrate that Demo2Reward effectively transfers to a real-world robotic learning scenario, enabling policy learning without manually engineering a reward function.
[2606.00083] From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
[Submitted on 22 May 2026]
Title:From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
View a PDF of the paper titled From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models, by Christian Gumbsch and 9 other authors
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Abstract:Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored the zero-shot reasoning capabilities of pre-trained Vision-Language Models (VLMs) as reward models. However, without careful prompt engineering, these approaches tend to produce suboptimal rewards, where false positive predictions can severely degrade downstream policy learning. In robotics, limited datasets comprising expert demonstrations are often collected to bootstrap policy learning. This scenario provides an opportunity to optimize a reward model prior policy training. We propose Demo2Reward a test-time adaptation technique to optimize the language instruction of a reward model based on a few demonstrations (3-10 trajectories) to reduce false positives while preserving true positives. Crucially, this requires no additional model training or computation resources during policy learning. We show that Demo2Reward consistently outperforms existing zero- and few-shot VLM reward models across a range of simulated robotic tasks and policy backbones. Finally, we demonstrate that Demo2Reward effectively transfers to a real-world robotic learning scenario, enabling policy learning without manually engineering a reward function.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2606.00083 [cs.LG]
(or arXiv:2606.00083v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.00083
arXiv-issued DOI via DataCite
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From: Christian Gumbsch [view email] [v1] Fri, 22 May 2026 16:04:22 UTC (2,393 KB)
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