Marius Memmel ✈️ CoRL 2026

Can an off-the-shelf VLM be a reward model for robot learning, with no robot data and no fine-tuning? Asked directly, VLMs tend to assume the task succeeded and report progress on failed runs. Fine-tuned reward models such as Robometer do better, but are small open models that must be retrained for every new base model. D&A from the University of Washington and NVIDIA gets a better zero-shot reward out of any VLM with prompting alone, by keeping the task hidden from the model that narrates the video. How it works • First, every object in the scene is listed, relevant or not. A describer then narrates the whole video with these names, without knowing the task or which objects matter. • An assessor sees only the instruction and that text, never the video. It first writes an explicit success criterion, then scores progress from 0 to 1 at each sampled frame. • No training. With Gemini models a trajectory costs 1.5-6 cents and 24-58 s of API calls, so it suits RL and data filtering, not closed-loop control. Does it pay off? • On the authors' D&A-Bench (54 real trajectories, half of them failures), Gemini 3 Flash with D&A reaches Kendall τ 0.67, vs 0.57 for Robometer. The same prompts also lift Qwen and GPT models, the weaker ones most. • As the dense reward for online RL on a DROID Franka, it takes a π0 policy from 10% to 100% success on putting the fruit in the bowl in about 90 min. TOPReward rewards the wrong object instead. Caveats • With Gemini 3 Flash, Robometer still wins on its own benchmark (0.648 vs 0.576). The SOTA claims need GPT-6 Astra, where D&A adds only 0.02-0.04 over a plain prompt. • Each real-robot result is a single task, and the paper gives no trial counts. Code is out (MIT). By @memmelma, @imankitgoyal, @Jesse_Y_Zhang, @fox_dieter17849, @AnqiLi24, @abhishekunique7 and co-authors.