# Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives

arXiv: https://arxiv.org/abs/2505.05787 · HTML: https://mlsanity.com/paper/2505.05787/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 43 of its 46 references that are in the map.
- First posted on arXiv 9 May 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023)
- [Diffusion Policy Policy Optimization](https://mlsanity.com/paper/2409.00588/) (2024)
- [Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models](https://mlsanity.com/paper/2310.10639/) (2023)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Understanding and Mitigating Copying in Diffusion Models](https://mlsanity.com/paper/2305.20086/) (2023)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [On Memorization in Diffusion Models](https://mlsanity.com/paper/2310.02664/) (2023)
- [DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics](https://mlsanity.com/paper/2210.02438/) (2022)
- [Detecting, Explaining, and Mitigating Memorization in Diffusion Models](https://mlsanity.com/paper/2407.21720/) (2024)
- [XSkill: Cross Embodiment Skill Discovery](https://mlsanity.com/paper/2307.09955/) (2023)
- [Equivariant Diffusion Policy](https://mlsanity.com/paper/2407.01812/) (2024)

## Built on by (most cited first, 9 of 9)

- [Much Ado About Noising: Dispelling the Myths of Generative Robotic Control](https://mlsanity.com/paper/2512.01809/) (2025)
- [A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies](https://mlsanity.com/paper/2604.13645/) (2026)
- [FALCON: Actively Decoupled Visuomotor Policies for Loco-Manipulation with Foundation-Model-Based Coordination](https://mlsanity.com/paper/2512.04381/) (2025)
- [Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness](https://mlsanity.com/paper/2603.21017/) (2026)
- [SketchPlan: Diffusion Based Drone Planning From Human Sketches](https://mlsanity.com/paper/2510.03545/) (2025)
- [What Do You Need for Compositional Generalization in Diffusion Planning?](https://mlsanity.com/paper/2505.18083/) (2025)
- [Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning](https://mlsanity.com/paper/2602.10594/) (2026)
- [Training-Free Imitation Learning with Closed-Form Diffusion Policies](https://mlsanity.com/paper/2606.01238/) (2026)
- [RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes](https://mlsanity.com/paper/2606.24403/) (2026)

## Abstract (arXiv)

Diffusion policies have demonstrated remarkable dexterity and robustness in intricate, high-dimensional robot manipulation tasks, while training from a small number of demonstrations. However, the reason for this performance remains a mystery. In this paper, we offer a surprising hypothesis: diffusion policies essentially memorize an action lookup table -- and this is beneficial. We posit that, at runtime, diffusion policies find the closest training image to the test image in a latent space, and recall the associated training action sequence, offering reactivity without the need for action generalization. This is effective in the sparse data regime, where there is not enough data density for the model to learn action generalization. We support this claim with systematic empirical evidence. Even when conditioned on wildly out of distribution (OOD) images of cats and dogs, the Diffusion Policy still outputs an action sequence from the training data. With this insight, we propose a simple policy, the Action Lookup Table (ALT), as a lightweight alternative to the Diffusion Policy. Our ALT policy uses a contrastive image encoder as a hash function to index the closest corresponding training action sequence, explicitly performing the computation that the Diffusion Policy implicitly learns. We show empirically that for relatively small datasets, ALT matches the performance of a diffusion model, while requiring only 0.0034 of the inference time and 0.0085 of the memory footprint, allowing for much faster closed-loop inference with resource constrained robots. We also train our ALT policy to give an explicit OOD flag when the distance between the runtime image is too far in the latent space from the training images, giving a simple but effective runtime monitor. More information can be found at: https://stanfordmsl.github.io/alt/.