DataMIL: Selecting Data for Robot Imitation Learning with Datamodels
Authors: Shivin Dass
Alaa Khaddaj
Logan Engstrom
Aleksander Madry
Andrew Ilyas
Roberto Martín-Martín
Venue: ICLR 2026 Poster (2026)
Keywords: Robot Learning
Data Curation
Imitation Learning
Primary area: applications to robotics, autonomy, planning
TL;DR: We learn to estimate each data-point's effect on imitation learning performance via Datamodels; we then leverage these models to select high quality samples that maximizes downstream policy success

Abstract: Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies. However, while these policies achieve strong mean performance across a variety of tasks, they often underperform on individual, specialized tasks and require further tuning on newly acquired task-specific data. Combining task-specific data with carefully curated subsets of large prior datasets via co-training can produce better specialized policies, but selecting data naively may actually harm downstream performance. To address this, we introduce DataMIL, a data selection framework built on the datamodels paradigm that reasons about data selection in an end-to-end manner, using the policy itself to identify which data points will most improve performance. Unlike standard practices that filter data using human notions of quality (e.g., based on semantic or visual similarity), DataMIL directly optimizes data selection for task success, allowing us to select data that improves the policy while dropping data that degrade it. To avoid performing expensive rollouts in the environment during selection, we introduce a surrogate loss function on task-specific data, allowing us to use DataMIL in the real world without degrading performance. We validate our approach on 60+ simulation and real-world manipulation tasks, notably showing successful data selection from the largest open collections of robot datasets (OXE); demonstrating consistent gains in success rates over prior works. Our results underscore the importance of end-to-end, performance-aware data selection for unlocking the potential of large prior datasets in robotics.

Published as a conference paper at ICLR 2026
DATAMIL: SELECTING DATA FOR ROBOT IMITATION LEARNING WITH DATAMODELS
Shivin Dass* UT Austin
Alaa Khaddaj MIT
Logan Engstrom MIT
Aleksander Madry MIT
Andrew Iyas? Stanford University
Roberto Martin-Martin' UT Austin, Amazon
ABSTRACT
Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies. However, while these policies achieve strong mean performance across variety of tasks, they often underperform on individ- ual, specialized tasks and require further tuning on newly acquired task-specific data. Combining task-specific data with carefully curated subsets of large prior datasets via co-training can produce better specialized policies, but selecting data naively may actually harm downstream performance. To address this, we intro- duce DataMIl., a data selection framework built on the datamodels paradigm that reasons about data selection in an end-to-end manner, using the policy itself identify which data points will most improve performance. Unlike standard prac- tices that filter data using human notions of quality (e-g., based on semantic or vi- sual similarity), DataMIL directly optimizes data selection for task success, allow- ing us to select data that improves the policy while dropping data that degrade To avoid performing expensive rollouts in the environment during selection, we in- troduce a surrogate loss function on task-specific data, allowing us to use DataMIL. approach
in the real world without degrading performance. We validate 60+ simulation and real-world manipulation tasks, notably showing successful data selection from the largest open collections of robot datasets (OXE); demon- strating consistent gains in success rates over prior works. Our results underscore the importance of end-to-end, performance-aware data selection for unlocking the potential of large prior datasets in robotics. More information at our website.
INTRODUCTION
Recently we have witnessed revolution in robot learning: inspired by the successes of large-scale language and vision models, the robotics community has begun training large foundation poli- cies (Brohan et al.) 2023 Black et al. 2024 | Bjorck et al.) (025) by amassing large diverse robotic datasets comprising of a variety of tasks, scenes, and robots (O' et al. 2024 Walke et al. 2023
Fang et al. 2024. The resulting generalist policies achieve strong mean performance across tasks and environments, but often underperform on individual tasks (Kim et al. 2024 (025), highlighting gap between generalization and task-specific competence. To bridge this gap, researchers have explored a post-training paradigm (Black et al. (024), where pre-trained foundation models are fine-tuned (Black et al., 2024; Kim al.) 2025) for specific tasks, though this process demands considerable number of newly acquired task speciffcc demonstrations. As datasets grow increasingly large and diverse, natural question arises: how can we identify and select data from within existing datusets to boost lask performance?
Selecting data to train a gigh-performig model is a complex endeavor. Naively, would require testing each subset of the data by retraining and evaluating the performance of the trained model. This is expensive for any sizable dataset, becoming infeasible in robotics, where evaluation involves
*: denotes equal contribution. 1: denotes equal advising Corresoondence to: sdass @aasssutexas... edu

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policy rollouts in the real world-a a time-consuming and often dangerous procedure. Prior data selection methods in robotics remove the dependency on policy rollouts by selecting data based on heuristics, i.e., assuming that the most useful data is the most similar in language description ZZa
et al. (022), visually (Memmel et al. 2024), in motion (Lin et al. (2024), or in state-action pairs (Du al. 2023 to a small number of task-specific demonstrations. While intuitive (and effective in some cases), these heuristics often make several assumptions and fail to consider the real impact of a datapoint on policy performance (see Fig. (left).
In other fields such as natural language processing (NLP) and computer vision (CV), researchers have developed an efficient framework for data selection based on model performance: datamod- els (Ilyas et al. 2022). Training a datamodel is a meta process in which the original learning algo- rithm and model are viewed as "black box" that consumes data, and the goal to train an estimator of the black box's 's behavior as a function of the input data. By avoiding selecting based on heuristics, datamodels stay close to the true optimization objective (trained model performance) while critically reducing the amount of training and evaluation steps necessary with the original learning algorithm to an acceptable level for NLP and CV. However, these evaluations are still infeasible for policy learning due the need for real-world rollouts, impeding their application to robotics.
In this work, we introduce DataMIL (Datamodels for Imitation Learning), a method that extends the success of datamodels to robotics by addressing the unique challenges of data-driven data selec- tion. DataMII. trains a data-quality estimator using a tractable surrogate objective. We demonstrate empirically that this objective retains sufficient correlations to the true objective (trained policy per- formance), allowing us to train datamodels that predict data influence without requiring expensive real-world rollouts. Moreover, thanks a process that remains closer the true objective and avoids assumptions about the characteristies of the useful data, DataMIL selects and curates datasets for hard cases (e.g.. different embodiments, multi-task settings) where prior heuristic-based methods are unsuitable. Across 50 tasks in MetaWorld ( (Yu et al.) 2020)), we show a 10% performance boost compared to the state-of-the-art baselines. We then show how datamodels can be estimated effi ciently for larger policies such as Octo (Octo Model Team et al. 2024) using improved datamodel estimators based on metagradients (Engstrom et al. 2025 and show task-specific dataset curation in ten tasks from the LIBERO benchmark (Liu et al. 2023 Finally, we effectively select data from OXE (O'Neill et al.] 2024), one of the largest open collections of robot datasets, demonstrating DataMIL's s efficacy in selecting datasets for new tasks and embodiments on real hardware.
2 RELATED WORK
Data curation for robot learning. Recent advances in robotics have leveraged ever-larger demon-
2023 Sharma et al. 2018), and on real hardware (Walke et al. 2023 Khazatsky et al. 2024] Mand et al. 2018 to train gener- alist policies capable of tackling diverse tasks (Brohan et al. 2023 Kim et al.) 2024 Black et al.) 2024 Bjorck et al., 2025). However, the sheer scale and heterogeneity of these datasets (varying robots, scenes, and objectives) has motivated a body of work data curation. For generalist train- ing, methods like Re-Mix (Hejna et al. (024) use DoReMi (Xie et al.) 2023) style optimization to learn optimal mixtures of data domains for improving model training, while others identify "high- quality" trajectories via mutual information criteria (Hejna et al. 2025) or by scoring samples with
stration collections-both in simulation (Mandlekar et al. 2021
policy rollouts (Chen et al. 2025).
Beyond generalist policy training, many studies have focused on task-specific dataset selection: given a handful of target demonstrations, one can sub-sample large datasets based on visual similar- ity (Memmel et al.] (2024), motion cues (Xu et al. (2022). or state-action closeness (Du et al.] (003)) Recent work (Kumar et al.), aims at fusing datasets aggregated via different similarity metrics by scoring each data subset and re-sampling them according to weight proportional to this score. While these approaches capture human notions of quality, they remain agnostic to each sample's ac- tual impact on downstream policy performance. Concurrent to work, CUPID (Agia et al. (025) influence measure estimated via online
focuses on single task curation by using a policy-gratde rollouts. In contrast, DataMIL demonstrates selection from large heterogeneous datasets, scoring datapoints entirely in an offline manner. This allows us to estimate each datapoint's 's contribution to final task success and curate training sets in an end-to-end, performance-ave aware fashion.
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Figure 1: Data selection with datamodels. (left) Similarity-basee methods select close samples (yellow), but these aren't It always beneficial for learning. DataMIL. evaluates data based on its impact on policy performance, selecting the samples that lead to policy improvement. (center) We estimate datamodels that score each sample by its influence on policy performance and select the highest- scoring samples for training. (right) DataMIL explores two datamodel estimation methods, adapted to robotics: regression and metagradient- based estimation (see Sec.
Datamodels and data attribution. Our work draws from line of work in machine learning data
attribution (Koh & Liang] [0177; Ilyas et al. 2024: Hammoudeh & Lowd 2024; Park et al. 2023
Chang et 2024] Ilyas Engstrom 2025) and, particular, the datamodels Tramework (llyas
et al. 2022 Park et al. 2023). At high level, this framework seeks to predict the behavior of ma-
chine learning models as a function of the data they are trained on. These ideas have been explored
in the fields of CV and NLP for improving language model pre-training (Engstrom et al. (024) and
instruction tuning (Xia et al.
[2024] Liu al. 2024 Engstrom al., 2025); for increasing worst- group robustness (Jain et al. 2024): and for removing outliers in supervised learning settings (Lin
jct al. (002)). Our work builds on this body of research, while-as we discuss in Sec. 4.2 also
tackling the unique challenges posed by the robot learning setting.
PRELIMINARIES: DATA SELECTION FOR POLICIES AND DATAMODELS
In this section, we first provide some general background on the problem of data selection for robot learning, and then describe the datamodels framework which our method is based.
Policy learning. The focus of our work is on the imitation learning problem. Here, our goal is learn a policy ı that maps states distributions over actions using a collection of training trajectories (or demonstrations) D = (T1, learning algorithm A as a function that takes as input dataset of demonstrations D and outputs IR,
TT)) of satte-action pairs. We will define policy
an optimized policy We measure the performance of the policy æ using metric M which is function mapping policies to a scalar value. The most common choice of metric is success rate- the fraction of times that sampling actions from the policy results in the policy completing given task our notation is general and can also capture other choices metric M.
Data selection in roboties. In data selection for robotics, we are given (a) a prior dataset D of demonstrations; (b) fixed learning algorithm AA (e.g., stochastic gradient descent on an imitation learning objective); and (c) a target metric M that we will use to measure policy performance. Our goal is to select subset of the data D' C D such that applying the algorithm A to the subset D yields a policy æ that maximizes the target M. Formally, we aim to find
arg mrg DCCD
(I)
Solving this optimization problem is challenging since algorithm A itself is expensive to compute (it involves training a policy on D'). Thus, exhaustive search over all subsets of is infeasible.
Datamodels. In this work, leverage data attribution (Koh & Liang 2017 Myas al. 2024
Park et al., 2023) Bae et al. 2024) (and specifically, the datamodels framework (yyys et al. al.] (022)) to tackle the data selection problem in Eq. The key idea behind datamodels is to directly approximate
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the target metric MM)(D)) as function of the data D', allowing answer questions like "what would the performance of the policy be if we trained on this subset of the data?" without actually training the policy. More precisely, a datamodel is a model f 2D - R that takes as input a subset of the data D' and outputs an estimate of M(A(D'))
Informal Definition (Datamodeling problem) Given prior dataset 'D and a learning algo- rithm A, the datamodeling problem the problem of predicting the behavior of model trained on a subdataset D C D without actually training a model.
had such an approximation in hand (assuming for now that we can compute one), we would approach the data selection problem in Eq. Tby solving the following optimization problem:
arg max f(D').
(2)
D'CD
This problem is much more tractable than the original problem Eq. because the datamodel is typically much cheaper to compute than the learning algorithm and target metric M(A((-)) For example, in supervised learning, recent works have shown that even linear ceaanodels-funtttin that decompose additively in terms of their inputs D D--can
can be accurate predictors of model perfor-
mance (llyas et al., 2022 Lin et al. 2022, Park et al. 2023 Bae et al. 2024: Chang et al. 2024
Intuitively, this means that we can assign a scalar value to each data point in that indicates how much it contributes to the performance of the learning algorithm A.
Since leveraging accurate datamodel f for data selection is straightforward, the main challenge in our work is constructing such a datamodel. That is, we need to find a way to build a function that can predict, with nontrivial accuracy, the performance of the learning algorithm A on any given subset of the data D' without actually training policy on that subset. In the next section, describe our methods for constructing such datamodel in the policy learning setting.
4 DATAMIL: DATAMODELS FOR ROBOT IMITATION LEARNING
An overview of our training and selection methodology is shown in Fig. I Below, we provide the details of each component, beginning with the estimation of datamodels (Sec. 4.1. adapting them to robotics (Sec. 4.2) and selecting data using datamodels train policies (Sec.
4.1 ESTIMATING DATAMODELS
In our work, we consider two ways of estimating datamodels: the regression method (Ilyas et al. 2022) and the metagradient method (Engstrom al. (0225). Both estimators approximate the out- come of model training linearly, in the sense that, for any training subset D' C DD the datamodel prediction f(D') takes the form,
i(D) Σ T(2)). RED
Intuitively, r(2;) captures the importance of the training example z; the target metric M(A(D)) (more precisely, 7(2) is the additive effect of 2; on the target metric). Linear datamodels are con- venient in the context of data selection, since they allow to solve Eq. by simply selecting the training examples with the highest scores 7 722)). Both estimators below take this form, differing only in how they compute T (z:)).
The regression estimator is straightforward but expensive way estimate datamodels- involves precomputing the scores T(2;) for each training example z; the broader training set D. Concretely, each of these datasets, we train a policy A(D,), and evaluate the target metric M. In our setting, evaluating M means rolling out the policy several times and computing the success rate. This gives us M, = M(A(D))) )) for each subset Dj. creating a dataset of m data performeroomance pairs (D,. for estimating datamodels. We compute the scores T(2;) for all training examples by solving the
Regression estimator.
first sample random subsets of the prior dataset Dj C D; for
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following minimization problem:
(r(.))...., = arg min
Σ
Σ JisiED;
TER"
Above, observe that the sum Σ E,, Ty is precisely the datamodel prediction of (A)())) when setting T22 = T3- Thus, Eq. corresponds exactly to linearly regressing the target metric onto the presence of each training point 24. More details are provided in App. B.2
Metagradient-based estimator. The regression-basees -baeidddd-d-ddd-----ee estimator requires training a large number of models, which quickly becomes computationally prohibitive for complex models such the ones used for modern visuomotor policies. A method to alleviate this challenge is to use a metagradient- based estimator. To that end, we parameterize the dataset by vector E [0, I[",, where w; specifies the weight of the i-th training sample (i is included in the dataset if = 1 and excluded if w/ 0)- Treating these weights as meta-pare neta-parameters, we write a first-order Taylor expansion our objective M(A(w)) from Eq.
[ as,
M(A(w)) & M(A(wo)) + Vw.M(A(wo)) (w wo),
(4)
where Wo is the all-ones vector, corresponding to training on the entire dataset. The gradient =
V Vw.M(A(wo)), known the influence function (Hampel 1974 Koh & Liang 2017), captures how changes in data weights affect model performance.
Naively computing I requires differentiating through the training process A(w) i.., unrolling policy optimization with respect to the meta-parameters w. This computation, known as the meta- gradient, has traditionally been notoriously difficult, and consequently most prior works in data tribution have focused on approximating Z (Koh & Liang] [0177] Park et al.) 2023 Bae et al. 2024). Recent advances, however, demonstrate that metagradients can computed exactly and efficiently by leveraging the iterative structure of standard optimization algorithms such as stochastic gradient descent (SGD) (Engstrom et al. 2025 Calian al., (025). Specifically, [Engstrom et al.. (2025) ex- ploit step-wise auto muto-diffeerentitioon combined with efficient data structures to keep the memory costs practical. The resulting influence scores I; quantify how positively or negatively the i-th training sample affects performance, and can be directly used as datamodel coefficients. [lyya & Engstrom (2025) shows that indeed, doing this reparaneterization and Taylor expansion with the metagradi- ent actually does give you very good estimates of model performance. A detailed description and pseudocode of the metagradient-
A(w)-ie.
estimator provided in App. B.3
4.2 ADAPTING DATAMODELS FOR ROBOTICS
While datamodels have been applied to language modeling (Park et al. 2023 Bac et al. 2024) and computer vision Kohh Liang. 2017 Tlas al. 2022 tasks, there are some unique chal lenges that we face when applying them to robotics. Below, we describe how DataMIL extends the datamodel framework handle robotic datasets efficiently.
Estimating datamodels without rollouts. In principle, the ideal target metric M is the policy's s true success rate under environment rollouts. However, real-world rollouts are expensive, and using them directly renders the objective non-differentiable, preventing the use of metagradient-base estimators that exploit diffcrentiability the evaluation metric. To overcome these limitations, we introduce a proxy metric M that (I) requires no additional rollouts and (2) is fully differentiable. Concretely, given small held-out demonstration set D Ptarget for the target task, we define:
M(T, Dtarget)
Σ
-LBC(T(s),
(5)
Dearget
(s.0)EDtarget
Where Lno(=(s), a) defines the policy loss on training example (s, (see App. D for the act objective for different policy classes). Hence, the true target metric M can be substituted by the proxy metric M in our original optimization (Eq lb. resulting in a more tractable and end-end differentiable objective for applying datamodels to robotic settings.
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A natural question is whether M is sufficiently good approx- imation of the true target to enable data selection. We study this question using the p ck-pl ace-wall 11 task from Meta- World ( Yu et al. 2020) (results on more tasks are provided in App. C. where we consider three different datamodel esti- mation techniques:
Target-Only
DataMiL-ng DataMiL
102
All-
DM-roloutts
Suctmards
i) DM-rollouts: using regression-hased estimator to estimate datamodel for the "true" target M (success rate via roll- outs);
ii) DataMiL-rg: using regression-based estimator to estimate datamodel for proxy target M (loss on heldout set);
m) DataMIL-meta: using metagradient- estimator to timate a datamodel for proxy target M.
Figure 2: Comparing true rollout success (M) vs. proxy metric (M)
We use each of these datamodels to select the top 10% of samples (as ranked by their estimated co- efficient (z:)) from a multi-task prior dataset consisting of a mix of expert and suboptimal demon- strations (see Sec. [ for details). We then measure the true success rate of policy trained on the selected samples, and visualize the results in Fig. 2 Our results show that (a) selecting data for the proxy metric M incurs only a marginal drop in final success; (b) applying the catagraddenn-bbsee estimator incurs another small drop in success but are significantly faster to train. Moreover, policies trained on selected data vastly outperform baselines trained the entire dataset Dtarget alone, achieving 7x higher success than the all-data policy, while target-only policy fails almost entirely.
These results demonstrate that (a) while validation loss is often noisy predictor of performance in robotics, it still provides a useful signal for data selection, and our proxy objective can effectively replace expensive rollouts, and (b) data curation is critical: naively using all data or only target examples yields suboptimal policies, whereas curated datasets substantially boost task success.
Clustering training examples. Datamodels measure the influence of each training example on overall policy performance. In robotics, a training example may range from single state-action pair, to sequences of state-action pairs (for sequential policies). Estimating influence the individ- ual training example level is often noisy: because each training example is seen only a few times during training. its individual effect on policy performance is often marginal and difficult to measure precisely. To reduce this variance, prior works clusters samples by class or task and then evaluates cluster-level influence instead of individual examples (Jain et al. 2023 Ley et al. 2024).
This is particularly suitable for robotics since data naturally clusters into state-action sequences, so we can group samples at different temporal scales-e.2 e.g. sub-trajectories (Memmel et al. 2024), tasks (Zha et al. 2024), entire domains (Hejna et al., 2024). Fine-gre grainerineee clusters offer precise selection but suffer from high noise, while coarse clusters yield more stable influence estimates at the cost of detail. We found that the optimal clustering strategy is function of the dataset size given a fixed compute budget: the larger the dataset, the fewer times each sample is seen during training, leading to noisier individual influence estimates. On mid-sized datasets like LIBERO, sub-trajectory clustering provides a balance between granularity and noise (see App. C.4), whereas on large-scale datasets such as OXE, aggregating trajectory-levee influence yields more reliable influence estimates.
Reducing distribution shift. Distribution shift is a particularly pronounced challenge in robotics: slight changes in lighting, camera pose, or robot dynamics can dramatically alter the data distri- bution. This issue becomes more severe when selecting data from large, heterogeneous datasets comprising of a variety of different embodiments, scenes, and tasks. Since datamodels rely on train- ing policies over the prior data to estimate their impact on target task performance, excessive shift between the training and target distributions can lead to poor datamodel estimates. To mitigate this, we include small fraction of target task data during datamodel estimation to better align the policy's learning with the target domain. Specifically, we split Diarget in half, include one half alongside the prior data for datamodel estimation, and reserve the other half purely for evaluating the proxy objective. We apply this technique only in real-world settings (i.c., OXЕ) since in simulation (i.c., MetaWorks LIBERO) the target data typically comes from similar distribution as the prior data.

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4.3 DATA SELECTION AND POLICY TRAINING
By applying our proposed modifications in Sec. to the datamodel estimators described in Sec. [4I] we obtain per-cluster attribution score on policy performance. While these attribution scores can be used in variety of different ways, in this work we use them to curate training sub- set: we select the top x% of prior examples with the highest positive influence to form Dsel- We then train the downstream policy и via behavior cloning Deet and Diarget using co-training recipe (Lin et al. 2024; Maddukuri et al., 2025) Khazatsky et al. 2024 Nasiriany et al. (022): at each training step, sample from Diarget with probability and from Dæet with probability 1-a.
In summary, DataMIL leverages datamodels (lyyss et al. 2022) to estimate how individual training examples effect policy's performance this estimation tractable and robust in robotics given task. We propose key modifications that make noise, avoiding expensive rollouts, and minimizing distribution shift. Given prior dataset 2> and target dataset Dargett we (1) Cluster the prior data D into trajectories or sub-trajectories, (2) Estimate influence scores using our proposed proxy metric, with the regression or metagradient datamodel estimators, (3) Select the top ranked clusters creating Dsel, and (4) Train a final policy co-trained on the target and selected data.
5 EXPERIMENTS
Datasets. We test DataMIL two widely used multi-task simulation benchmarks: (1) Meta- World Yu et al. 2020), contains a suite of 50 distinct robot manipulation tasks, and (2) LIBERO benchmark (Liu et al. 2023). consists of 100 tasks with diverse objects, layouts and scenes. In the real world we test using the Open-X Embodiment (OXE) datasets (O'Neill et al.] (024) an aggregation of diverse robotic datasets collected across various robots and labs around the world.
Training and Evaluation Details. We use the language-condttioone Octo (Octo Model Team et al., 2024) model in LIBERO and OXE settings, initializing the model with a pretrained checkpoint provided by the authors to speed up training. For MetaWorld, we use the environment state as policy input, and hence use simpler MLP based policy with a Gaussian action head, and study both goal-conditioned and no-conditioning settings. Results for the latter can be found in App. C.2
Baselines. We compare DataMIL the following prior works: Behav- iorRetreival (BR) (Du et al. 2023) trains VAE on state-action pairs and uses similarity with the target data in the latent space to retrieve
Target Only
BR STRAP
Al-Dala
AR
DataMIL
single state-action pairs; FlowRetrieval (Flow) (Lin et al. (2024) uses similar approach but trains the VAE on the flow features of the images computed using GMFlow (uu et al.) 2022); STRAP (Memmel et al. 2024) uses features from a Dino V2 (Oquab et al. 2023) model and uses dynamic time-warping to retrieve similar tubttrajectories We also in- troduce a simple action retrieval (AR) heuristic that retrieves based on action sequence similarity. Finally, train policies only the target data (Target-Only), and co-trained with all data (All-Data) to measure the overall importance of data selection.
(a) Avg. success over 50 MeraWorld Tasks
5.1 RESULTS
How does data selected using DataMIL impact policy performance? Metaworld. MetaWorld's 50 manipulation tasks offer rigorous testbed for data selection. We construct our prior multi-task dataset D by com- bining (1) expert demonstrations generated by scripted policies and (2) lower-quality exploration trajectories sampled from the replay buffer of multi-task task SAC agent trained across all tasks (see App. [.I for details). For each task, we use expert demos as Dtarget. and use the regression- based datamodel estimator to select the top 10% of samples (Sec. 4
(b) Avg. success over LIBERO-10
Figure 3: Performance of policy trained on se- lected datasets in sim. environments.
This setup is challenging as the selection method must both identify rele- vant tasks and filter noisy, suboptimal actions from the autonomous data. In Fig. Ba we report policy performance averaged over all 50 tasks. Similarit--bssdd baselines perform poorly: state-only (SR) fails to
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Average
Franka-Ball
Franks-Pouch
Tago-Sink
Droid Mutttskk
Target Ony
Random
AR
BR
STRAP
DataMIL
Figure 4: Results on OXE. We evaluate policies trained on subsets of the Open X- X-Embodiment dataset selected by different strategies. DataMIL consistently achieves the highest success, high- lighting the need for end-end policy-aware data selection techniques. (Droid-Multitask shows av- erage success; per-task results in App. C.7
ject poor actions, action-only (AR) selects irrelevant tasks with similar action distributions, and state-action retrieval (BR) gives equal weight to both modalities, which may not be the appropriate provvddes qualitative analysis). In contrast, by directly estimating each sample's 's influence on policy performance, DataMIL effectively identifies useful demonstrations and discards harmful samples.
recipe for all tasks (App. AA. provides
Can we scale DataMIL larger and more complex policy classes?
LIBERO. We test DataMIL on 10 long-horizon tasks from the LIBERO-10 setting, using LIBERO- 90 (comprising 4500 tumaa-teleoperated demonstrations) as the prior dataset and selecting 10% of the data (App. C.3 compares other selection %). The complex tasks and the high-dimensional RGB observations LIBERO demand powerful policy; we use Octo (Oeto Model Team et al.] 2024), transformer-bast based diffusion policy. Training Octo is costly, making the regression estimator (which requires retraining across many subsets) impractical, and so we employ the metagradient datamodel estimator (Sec.
Fig. 3b compares success rates of policies trained on the data selected by DataMIL. with the base- lines in each of the 10 target tasks. The relatively clean structure of LIBERO single- view, single cmbodiment-maker
favorable for baselines that select via visual similarity (eg. STRAP, BR and Flow). However, observe that their effectiveness varies significantly across tasks, likely due to the task-dependent suitability of each heuristic (task-wise success rates are provided in the appendix Tab. In contrast, DataMIL consistently performs well across all tasks, achieving the highest average performance overall.
Can select data from large heterogeneous datasets in the real world?
Open X- X-Embodiment Dataset (OXE). In the real world, we show data selection from the OXE O'Neill et al. 2024) datasets and evaluate on four tasks on two robot embodiments shown in Figure 4 (top). This setting is particularly challenging: OXE is a heterogeneous aggregation of data from different labs, robots, camera setups, lighting conditions, and object arrangements. Fur- ther, none of our test tasks appear in OXE we avoid matching the scene, camera pose, or objects. Instead, we aim understand whether seemingly unrelated prior data can still yield positive transfer when curated appropriately. Our base setup uses 24 OXE datasets that were part of Octo's 's original training. For the Franka-Ball and Franka-Pouch tasks, we subset this spectively (denoted as OXE-13 13 and OXE-23). Full details on the number of target demonstrations, dataset partitions, and evaluation methodology are provided in App. F3
and 23 datasets, re-
Due to the scale of OXE, we replace the All-Data baseline with Random baseline that randomly samples the same number datapoints as DataMIL and other methods. In Figure we observe that DataMIL effectively selects relevant data even from highly heterogeneous sources. In the simpler Franka-Bas Ball task, visual similarity-bast based baselines perform competitively. However, as the dataset grows diverse-as in Franka-Pous -ouck- these heuristies begin to break down, while DataMII.
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Benoday Speic
Autin
Nuth Mainn
(a) Data selected by DataMIL
(b) Data selected by AR
(c) Data selected by BR
Figure 5: Distribution of datasets selected by different methods for Tiago-Sink task
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(a) Data selected by DataMIL
(b) Data selected by Flow
(c) Data selected by BR
Figure Distribution datasets selected by different methods for Franka-Pouch task
continues identify data that improves policy performance. In the Tiago-Sink task, explore a harder setting, selecting data for the Tiago (Pages et al. 2016) robot, an embodiment that never appears in the prior data. Despite this, DataMIL able select cross-embodiment demonstrations that improve task success. We discuss this further in Sec. 5.2 Finally, we move beyond single-task selection in the Droid-Multitask setting, where the target comprises three tasks: bread in bowl, napkin in drawer, and open drawer. This setting tests whether a single curated dataset can sup- port multiple downstream objectives simultaneously. DataMIL consistently outperforms baselines, selecting data that improve its performance all tasks, yielding a stronger average overall.
These results highlight that DataMIl. scales to real-world robotics, handles heterogeneous datasets, and supports both single-task and multitask learning- even in settings with unseen embodiments.
5.2 WHAT DATA IS SELECTED BY DATAMIL?
Here we discuss and qualitatively analyse the data selected by DataMIL from the OXE dataset. More visualizations are shown in App. A.3
Type of embodiments selected. DataMIL is able to select useful data for a completely new em- bodiment the Tiago-Sink experiment. In Fig. 5a we show the highest frequency datasets selected by DataMIL and observe that even though they are visually quite different (Fig. [0c), sampled from datasets such RT-1 (Brohan al.) 2022), BC-Z (Jang et al., 2022) and Bridge (Ebert al.) [02T] Walke et al. 2023), they still represent the essence of the target task robots operating on table top from an ego-perspective present in the prior dataset (e.g., Franka), the selected data often comes from unrelated domains. For instance, in Franka-Pouch (Fig. (6). Flow selects data from BC-Z (Google Robot), and BR retrieves from Bridge (WidowX). This could be due the baselines placing more weight the scene/distractors when computing similarities. In contrast, the top five most frequently selected datasets by DataMIL are all sourced from Franka (Fig. 6a).
For baselines, we observe that even when the target embodiment
Distribution selected data. Figures 5 and 6 we show the distribution of datasets selected by DataMII. and representative baselines for the Tiago-Sink and Franka-Pouch tasks, respectively.

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We find that data selected by DataMIL usually balances several different datasets, whereas most baselines select a majority their data from a single source. For example, AR retrieves most of its data from RT-1 in the Tiago-Sink task (Fig. [5b), while Flow disproportionately selects samples from BC-Z for Franka-Pouch (Fig. 6bb. In contrast, DataMIl. consistently selects data across a broader range of datasets in both of these cases (Fig. 5a andd We hypothesize that since there is no data that exactly matches the target task, the selected data must not only be relevant but general, so as enable positive transfer in capabilities and not make the policy overfit single type of domain.
Top and bottom samples. Analyzing the the highest and lowest ranked datapoints by DataMIL in Fig. [0 we find that they typically look similar (c.g., same embodiment or dataset). This makes sense: similar states can have very different action distributions, and while some of these actions might help reduce the policy loss on the target data, the others might lead to a large deviation, making them harmful for final policy learning. This aligns with data attribution works in computer vision, where harmful data looks very similar to helpful data but with different labels (Ilyas et al.) 2022 Feldman & Zhang 2020). Understanding how and why these fine-grained differences affect data selection, and ultimately, y, the policy performance, is interesting direction for future work.
6 CONCLUSION
We present DataMIL, data-driven method for data selection for imitation learning. DataMIL builds upon the framework of datamodels, which has been applied successfully to data selection in NLP and CV, and extends it to real-world robotic applications. Extensive experiments in simulation and real-world settings empirically support that DataMII. retrieves data to train higher-performing poli- cies than multiple existing state-of-the-ar baselines, particularly in complex scenarios. At the same time, several limitations remain. First, despite employing an efficient peetagradient-basee estimator, estimating datamodels still incurs computational costs several times higher than training a policy on all data (see App. G), making scalability an important challenge. Accelerating datamodel training, for instance through smaller proxy models (Khaddaj et al.) 2025), promising direction. Sec- ond, DataMIL. (and data selection techniques for robot learning more broadly) relies on a range of hyperparameters (e.g., target dataset size, clustering sizes) for which we currently lack strong intu- itions. We expect future iterations and broader adoption make these choices more principled and user-friendly. Finally, while we demonstrated selection from large prior datasets, our target settings were primarily single-task. Although the Droid-Multitask setting partially addresses this, extending evaluation to truly large-scale, multi-task robotics settings remains an open and impactful avenue. Addressing these limitations will be crucial for scaling DataMIL to practical robotic applications.
REPRODUCIBILITY STATEMENT
We have taken several steps ensure the reproducibility of our results. Sec. desscribs all experi- mental settings in detail. In addition, App. [ provides step-by-step descriptions and pseudocode for estimating datamodels with both regression- and metagradient-basel approaches, App. IFi outlines the full experimental setup for our simulation and real-world environments, along with the composi- tion of the prior datasets, and App. Hists key hyperparameters we use in DataMIL for each setting. Together, these materials are intended to facilitate transparent replication of our findings.
ACKNOWLEDGMENTS
We thank Luca Macesanu for helping with some of the real world evaluations and Bowen Jiang for his feedback on the manuscript. We also thank Jullian Yapeter and Karl Pertsch for their assistance in generating the Meta World dataset. Work supported in part by DARPA TIAMAT program (HR0011- 24-9-0428) and also supported by the NSF grant DMS-2134100 and Open Philanthropy.
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A QUALITATIVE RESULTS: WHAT DATA IS SELECTED BY DATAMIL?
(a) Composition of data selected via DataMIL
(b) Composition of data selected via SR
Suzepeima Dimas
(c) Composition of data selected via AR
(d) Composition of data selected via BR
Figure 7: MetaWorld Qualitative Results. Percentage of data selected from each task and ex- pert/suboptimal source for DataMIL, SR, AR and BR
A.1 METAWORLD
As described in Appendix F.I our prior dataset for MetaWorld combines both expert and sub- optimal demonstrations. our experiments, we retrieve the top 10% of this data ranked by DataMIL to train the policy. Here, we qualitatively examine the dataset selected by DataMIL for the pick- place-wall task.
Figure 7 shows the percentage of data selected from each task and data source (expert subopti- mal). We observe that SR, while able to retrieve samples from relevant tasks, fails differentiate between expert and sub-optimal demonstrations--esullee low-quality data. In contrast, AR filters out sub-optimal samples more effectively by matching ac- tions, but it lacks task awareness due to its disregard for state information, often pulling data from irrelevant tasks. BR, which embeds both state and action features jointly, exhibits a blend of SR and AR behaviors- capturing elements of both but also inheriting their limitations. In compari- son, DataMIL consistently selects data from the correct task (green bar) while also avoiding noisy, sub-optimal examples.
in the inclusion of large fraction of
A.2 LIBERO
In LIBERO, for two target tasks, Book-Caddy (Figure K and Bowl-Cabinet (Figure we investi- gate the proportion of data that is selected from each of the prior tasks in LIBERO-90.
Target Task
2ASk coppatmmett of the
CLoRs coninet
the codte
Pick up the book and
place 1t in the back
compastnans of the caddy
Figure 8: LIBERO Bool-Caddy. Proportion of prior tasks selected for the book-caddy task
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Target Task
Pst the Elact toal top
Clowe the bettow drseet Use top desves
FLRK UP the seded LED 1035
Put the blact bostl the boston draver the olose
Figure 9: LIBERO Bowl-Cabinet. Proportion of prior tasks selected for the bowl-cabinet task
Since the exact target tasks do not appear in the prior dataset, we manually label (in orange) tasks with similar semantics and object layouts. We also visualize examples from the most and least frequently selected tasks. The plots support empirically that DataMIL consistently selects data from tasks with matching object and scene configuration. The visualizations also reflect this, showing that related tasks from similar looking scenes, albeit with related but different language instructions, most frequently selected, aligning with intuition developed in prior works (Saxena al. 2025 Maddukuri et al. 2025).
A.3 OXE
(a) Franka- Ball
(b) Franka-Poue Pouuch
Tareet Obs.
Targer
Bettom
top
Bottom
(c) Tiago-Sink
(d) Droid-Multitask
Target Obs.
Targat Obt.
Top
Fettom
TOp
Bortor
Figure 10: Top and bottom ranked samples by DataMIL for each of the real-worls tasks
Figure I0 visualizes the highest and lowest ranked datapoints identified by DataMIL for real-world tasks. As discussed in the main text, these examples often appear visually similar, aligning with findings in computer vision (Iyas et al. 2022 Feldman Zhang, 2020) where data points that look when visually
alike but carry different labels similar observations correspond to different actions, thereby confusing the policy. How and why these gra-ggraindd differences affect data selection, and ultimately, the policy performance, is an interesting direction for future work.
mislead the model. In roboties, this may
B ESTIMATING DATAMODELS
In this section, describe the datamodeling framework in more detail. In particular, first pro- vide the formal version of Informal Definition[] then describe the estimators that we use to construct datamodels in this work, namely the regression estimator and the metagradient-based estimator.
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B.1 FORMALIZING DATAMODELING
The goal of datamodeling is to construct a function / that can predict the performance of a learning algorithm A any given subset of the data D' D without actually training a policy that subset. Let 'D be a prior dataset of imitation-learning data of size N = =PD,, and let us represent if the i-th training sample in
any subset of as binary vector w E {0, 13 where wy the subset and wy = 0 otherwise. Let a learning algorithm A be function that takes as input dataset (represented as binary vector w) and outputs a policy A( ()). The datamodeling problem to construct a function f that can predict the performance of A(w) when trained on any given subset of the data without actually training policy on that subset. More formally, aim to find function f minimizing the following loss:
E Bernoolll 3 (M((A())-
(6)
where M is the target metric and Bernoulli( ()) is a random binary vector of length N. Recall from the main text that we are particularly interested in datamodels f that are additive in the training dataset-in in terms of our formalization, we are interested in functions f of the form
f(w)=w'
for some vector 3 E RR, An estimation method for datamodels is thus just a method for finding good estimate of the vector 3. We refer the reader to llyas et al. (2022) for more detailed discussion of datamodeling.
B.2 REGRESSION ESTIMATOR
The regression estimator is a simple yet effective method for estimating the vector 3 that treats datamodeling supervised learning problem. In particular, the regression estimator first samples a set m binary vectors Wi,..., Wm ~ Bermoullipp)" where p is the probability of inclusion; for each of these binary vectors, trains a policy A(w,) on the subset of the data indexed by w., and evaluates its performance using the target metric M. It then fits a linear model to the performance of these policies on the sampled binary vectors, i.e., it solves
min BERN
Σ (M(A(w:)) -., 99)
(8)
and uses the resulting vector 3 as the parameters of the datamodel j(w) = T The cost of build- ing this estimator is high, since it requires training policy for each of the binary vectors, but [lyas ctt al. (2022) shows that the estimator can be very accurate, and indeed identifies highly influential subsets of the prior dataset. Pseudocode for the regression estimator is provided in Algorithm
B.3 METAGRADIENT--ASSI ESTIMATOR
The metagradient- estimator is a more sophisticated method for estimating the vector 3 that avoids the high cost of training a policy for each binary vector. Instead, the metagradient-basel estimator operates by leveraging a classical statistical tool called the influence function (Hampel) 1974]. Intuitively, the metagradient- based estimator proceeds as follows. First, instead thinking of training datasets as binary vectors w E {0.1) we think of them as real-valued vectors [0, 1] where each coordinate w; corresponds to the importance weight placed on the i-th training sample in the dataset. Concretely, if w; = 0, then the i-th training sample is not used in the training set, and if w; = 1, then the i-th training sample is used in the training set with full weight: 0 wi 1, then the i-th training sample is used in the training set but its loss is scaled by 24- Observe that this parameterization is equivalent to the binary parameterization for w; E {0, 1}. but gives continuous way to represent the training set.
Once we have this continuous parameterization, we can write the first-order approximation to M(A(w)) as,
M(A(w)) M(A(wo)) + v.((A(wo)) wo),
(9)
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Algorithm 1 Regression Estimator
Algorithm 2 Metagradient-base Estimator
Input: Datasct D of size N, policy leamer A(-),
Input:
initial included data c = 1n. policy learner
target metric M(+)
A(-). target metric MM-)
Hyperparameters: binary masks m, prob. p
Hyperparameters: step size number of steps T
Generate training pairs for the surrogate
P. Gradient Storage
G G+D 0 P Meeagradiint descent loop
for i 1 to m do
Sample w; ~ (ermoullip))* N
for t < 1 to T do
Train policy subset:
A(ws)
w w 0,
Evaluate performance: yr < M(T.))
Compute metagradients (2M((((())
Append pair: 7' + T + (W., y6]
g
Ow Sample Bernoull(pp)
Fit linear datamodel using Eq.
Store g: G f- G + [g @ m]
@ <
arg min BERN MI
(y w: 13)
& Update Counts
sign(g) @ m
return
return G
where Wo is the vector all ones. The gradient V.M(AA( wo)) is known as the influence function, and gives linear approximation the loss in Equation 6 That is, if we could compute the influence function exactly, we could use it as a datamodel directly, i.e.,
f(w) - M(A(wo)) v.M(A(wo)) (w wo).
(10)
Traditionally, the influence function is notoriously hard to compute, and so prior work on data
tribution has focused on approximating it (Koh & Liang] 2017 Park et al.) 2023; Bac et al. (024).
However, recent work has shown how to compute exactly and efficiently (Engstrom et al. 2025) and how to use this exact influence function as a datamodel estimator (Ilyas & Engstrom 2025).
Observe that in order for the metagradient- based estimator be valid, the function ((A(w)) (w)) must be differentiable with respect to w. For this to be satisfied, it is sufficient for (a) the target metric M be differentiable with respect to the policy A(w), and (b) the policy A(w) to be trained via an iterative algorithm composed of elementary differentiable operations (which is almost all of the popular off-the the-shelf learning algorithms).
Gradient descent on training data. To operationalize the metagradient- based estimator, we use the Metagradient Descent (MGD) algorithm (Engstrom et al., 2025), to compute influence scores and iteratively refine the dataset. The algorithm views data selection the selected data counts e E {0, 1) N where c = indicates inclusion of the i-th sample.
optimization problem
Given count vector c, let Ag denote the policy obtained by training the dataset defined by c, and let ((Ae) denote its evaluation on the target metric. MGD introduces a differentiable surrogate A', (w), where E N perturbs the per-sample weights during training only certain iteration k. while keeping the rest of the training as is. By construction, setting w = 0 recovers the original training procedure, i.c., A0) Ac-
We can then compute the infiuence/metagraddentt as,
g V (VA,(((((w))
(1I)
w=o'
which measures how an infinitesimal upweighting of each training example at the kth iteration would affect the downstream performance. The sign of thus reveals whether increasing or decreasing the weight of sample would improve M.
To update the dataset, MGD applies stochastic coordinate updates: random mask m Bernoullipp)" ( selects a subset of coordinates to update, and the counts are modified as
c siinigg m,
(12)
where @ denotes elementwise multiplication. This step is analogous to performing gradient descent over training samples with p the learning rate. Repeating this process iteratively over 7 steps
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Thrget-Orly
Alk Dats
DM-ntlouus
DataMil-nng
100
Success
60
plck pacc-wall
box-ckose
p4g unplagg side
dial-uun
Figure 11: Extended Fig. Results. Comparing true rollout success (M) proxy metric (M)
refines c, yielding dataset concentrated on the most influential examples. In our experiments, we found that averaging the gradients g computed over the 7' iterations lead to more reliable influ- ence scores. A pseudocode of the algorithm is provided Algorithm 2 and we refer the reader to Engstrom et al. (2025) for more details.
C COMPARISONS AND ADDITIONAL RESULTS
Here we provide some additional results and show comparison of some key hyperparameters such as percentage of data selected, cluster size and more.
C.I COMPARISON: TRUE ROLLOUT SUCCESS vs. PROXY METRIC
Recall from before, that we are comparing the data selected using datamodels computed via our proposed proxy metric in Equa- tion 5 (DataMIL-rrg vs. true policy rollouts (DM-rollouts). We make this comparison in 4 tasks from MetaWorld as shown Figure [[] where we select the top 10% of samples (as ranked by their estimated datamodel coefficients) from multi-task prior dataset consisting of mix of expert and suboptimal demonstrations (dataset details provided in Section F.I and measure the success rates of policies trained the selected data. Across all tasks Section (a) our proxy metric only incurs a small drop in performance across all tasks, thus demon- strating its efficacy all data or only target examples yields suboptimal policies, whereas curated datasets substantially boost task success.
Here we provide extended results of our analysis in Figure 2
observe similar trend discussed in
viable metric for data selection and (b) data curation is critical: naively using
C.2 METAWORLD NO-GOAL CONDITIONING EXPERIMENTS
In Figure of the main paper, we presented results on MetaWorld with goal conditioning, where policies receive explicit goal information pro- vided by the simulator. Gooll-codditooing is often essential in settings like LIBERO and OXE, where the target task has limited demonstrations and generalization from other tasks is required. However, in Meta World, the prior dataset already includes expert demonstrations for the target tasks. Therefore, an effective data selection method should be capable of retrieving relevant examples even in the absence of goal informa- tion.
Target Only
AR
Aata
BR DotaMIL
To test this, we repeat the MetaWorld experiments described Ap- pendix [.I but mask out goal states during both data selection and pol- icy training. Results averaged over all tasks are shown Figure 12 Even without goal-conditioning, DataMIL continues outperform the best baseline by 10% in average success rate. However, overall per- formance across all methods declines compared the goal-conditioned
Figure 12: Avg. suc- cess on MetaWorld with- out goal conditioning.
setting (Figure Baj. highlighting that, without goal information. selected data from other tasks can introduce harmful interference during training.
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Cated4
Clusten16 Cluseno0
DaSML
Al-all
83
Trejedary
00000d Rote
Rade
Saccass
40
40
57
1056
203
0076
Scal-G ScaliCCrrar Maghl Soup-C Average
20
0.3
0,5
0.7
Selected Date Parcart
Teadss
Co-Traniig Rato
(a) ablating the selected data %
(b) ablating the cluster size
(c) ablating the co co-training ratio
Figure 13: Ablation experiments of DataMIL over different key data selection and training hyper- parameters. Each experiment is averaged over 5 seeds on each of the tasks in LIBERO-5. In (a) and (c) each data point represents averaged success rate over all tasks.
C.3 COMPARISON: PERCENTAGE DATA SELECTED
A key hyperparameter in data selection is the proportion of prior data to select. Unfortunately, there is no principled way to determine this value other than training and evaluating policies across different selection ratios. To enable systematic ablations, we construct a smaller benchmark within the LIBERO suite, denoted LIBERO-5, consisting of five tasks: Book Sook-Caddy, Rowl-Cabinet, Cream- Butter, Mug-Microwave, and Soup-Cheese.
Figure 13a reports the average performance DataMIL and two baselines, BR (Du al. 2023) and Flow (Lin et al.] 2024), on LIBERO-5 while varying the percentage of prior data selected. At low selection ratios (5% and 10%), DataMIL substantially outperforms the baselines, highlighting its ability to prioritize hugher-quality samples compared to couristic-based methods. At higher selection ratios (2 20%), the performance gap narrows and all methods achieve comparable results, as the majority of useful data is eventually included, diminishing the impact of the selection strategy.
C.4 COMPARISON: CLUSTER SIZE
As described in Section 4.2 we group individual state- action pairs into temporal clusters before estimating their influence on policy performance via datamodels. We evaluate the effect of clus- ter granularity on LIBERO-5, with results shown in Figure [3b] Specifically, we compare three strategies: Cluster-15 (sub-trajectories of length 15), Cluster-30 (sub-trajectories of length 30), and Trajectory (entire trajectories as clusters). We observe that finer- finer-grained clustering yields higher performance, as it provides more precise control over which segments of data are selected. Accord- ingly, our main experiments adopt a cluster size of 15.
C.5 COMPARISON: CO-TRAINING RATIO
While the data selected by DataMIL can be leveraged in multiple ways, we adopt co-training strategy that combines target data Dtarget and selected data D.el- At each training step, batch is drawn from gearget with probability and from Deet with probability not intrinsic to DataMIL, it influences downstream performance. Figure [3c shows the average LIBERO-5 results of DataMIL and the All-Data baseline across different co- co-training ratios a. We find that = 0.5 consistently yields the best results. Given the similar setup between our simulation and real-world experiments, and the high cost real-world evaluations, we adopt a 0.5 for real-robot experiments well.
a. Although this choice
C.6 COMPARISON: LONGER TRAINING FOR ALL-DATA BASELINE
Since the All-Data baseline leverages substantially more training data than other methods, also investigate whether it benefits from extended training. On LIBERO-S, we compare training du- rations of 1 1x, 2x, and 4x the number of steps used for other methods. The resulting average
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Tasks
Target-Only
All-baaa
DataMIL
LIBERO Evaluations
Soup-Sauce
38
15.10.7
43.6
Bowll-ahinet
69.2
Mug-Mug
0.1 0.9
2.44
4.8
2.8
6.5
3.2
0.8
Book-Caldy
8.3
58.1 10.7
65.2 13.1
7.6
09.2 20.2:13.2
83.2 7.8 7.8
82.4
Mug-Pudding
2.0
2.4 1.7
5.9
2.2
5.8 3.5
10.8 6.9
4.8 3.3
Soup Claesee
3.3
22.0 4: 6.5
29.3 10.4
35.2
5.2
26.8 4.8
35.2 3.9
36.0 6.0
Mokz-Moka
5.8
12 8.2
4.8
15.2 4.1
5.5
6.8
12.0 5.5
Mug-Mienoaaee
27.6 18.5
35.6 10.4
29.2 10.6
43.2 9.9
9.9
38.8 6.2
31.4 8.3
39 12.7
Libero-Average
21.5
27.72
30.52
36.42
34.96
37.76
ONK Evaluations
Franka-Ball
Franka-Pouch
17.6
Tiago-Sink
Droid (Drzwer)
0.0
0.0
0.0
20.0
70.0
55.0
75.0
Droid (Bread)
4.2
33.3
20.8
0.0
41.7
16.7
41.7
Droid (Napkin)
25.0
45.0
40.0
45.0
40.0
35.0
65.0
Droid-Multitss
9.4
26.6
20.3
20.3
50.0
31.4
59.4
Real-Average
20.4
30.9
21.1
39.4
40.0
39.8
61.0
Table 1: Numerical results for LIBERO and OXE evaluations
performances are 43.36, 44.13, and 38.40, respectively. These results indicate consistent benefit from longer training; in fact, excessive training can degrade performance due to overfitting. For fairness, we therefore use the same number of training steps across DataMIL and all baselines.
C.7 NUMERICAL RESULTS AND DISCUSSION
Task-wise numerical success rates for the LIBERO and real-world settings are reported in Table Our results on LIBERO differ from those reported the original STRAP paper on similar setting. and we attribute these differences to two key factors:
1. Evaluation Protocol. We suspect that the main contributing factor is likely difference in the evaluation protocol. In the original STRAP implementation, the authors evaluate multiple training checkpoints and report results from the best-performing model. This can lead to higher reported success rates. In contrast, our evaluation protocol follows a stricter setup: evaluate the policy only once, the final checkpoint after training completes, without any checkpoint selection.
2. Policy Architecture. While we use the original STRAP data retrieval code, we differ in the policy architecture used for imitation learning. Specifically, train with Octo (Octo Model Team et al. 2024). large transformer- transformer-basee policy from Robomimic (Mandlekar al. 2021) with Gaussian mix- ture head. These two models have different inductive biases, which can lead to variation in performance across tasks. For example, when trained solely on the five target demon- strations from the moka-moka task, Octo achieves a 6% success rate, while Robomimic achieves 0%. However, in the mug-mug task, the Robomimic policy reaches 38% success, while Octo performs close to 0%.
diffusion policy, whereas STRAP uses
We believe that both protocol differences and model architecture contribute the gap in reported numbers, and since our goal is to study data selection, our results reflect fair and consistent evalu- ation under unified training and assessment setup across all baselines.
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D PROXY METRIC DETAILS
Recall from Eq. that our general proxy metric is
M(T, Drarget)
E
LBC
DDrareet
(s.0)EPtarnet
where LBC is the behavior-cloning loss appropriate the policy class. Here provide how the equation looks like for specific policy classes that we used our experiments.
Werld. We parametarize the policy in MetaWorld
aa(a 8) = M(; no, (s), diag(oo, ())
and consider two forms of LB::
1. Negative Log-Likelihoon (NLL):
LNLL(S, a) = -lo To(a (a|
2 (a Ho, (s)) E(s) (a- (a-Ma, + log det (2t I(s))
(13)
2. I loss:
Le (8, ||a ne, (+)ll
(14)
Empirically, the /1 loss works better for the regression estimator, while metagradient- based estima- for performs well with the NLL loss.
LIBERO and OXE. In the LIBERO and OXE settings, we use Octo as the learning model which transformer- based policy with diffusion action head. It's behavior-cloning loss is the standard denoising score-matee matehint objective:
Laitt (sit(s,,0 a) Bu -elee-eeevaaee (vaaa v1 - ãr 8, 1)
where ag 10 the heward-process noise schedule, ā I ag and co t) is the network's 's noise prediction.
Substituting LBC Caift into the proxy metric gives,
E co(ora+ v1 ōes, 8, 1/| (15)
M(T, Dearget) =
Σ
[Dearget|
(s,a)E Dearget
D. UP-WIIHITING RELEVANT STATES
In robotics, more often than not we have some information about what states and actions are of higher importance than others, for example states closer to object interactions may be more relevant than moving around in free space. Our proxy metric provides a seamless way to incorporate prior knowledge by re-wen ce-weighting important states in the behavioral cloning loss. Concretely, introduce a state-action -eependdddnd weight w(s, a) into the objective:
M(ST Dtarget)
w(s, (BC(,))
|Daagett| (s.a)EDrarget
By default, we set wss,, a) = and use the unweighted proxy. In the LIBERO experiments, however, found that doubling the weight for states immediately preceding grasp significantly improves data selection. Thus, for those "pre-grasp" states we use w(s a) = 2, while all other states retain w(s, a) 1.
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E BASELINE IMPLEMENTATION
Implementation details of the baselines are provided below.
2023): FlowRe- FlowRetrieval (Lin et al. 2024) and BehaviorRetrieval (Du et al.
trieval (Flow) and Behavior Retrieval (BR) baselines compute similarity on the image flows and state-action pairs respectively. Since these features typically include high-ding sigh-dimensional image observations. Flow and BR train VAEs to encode the features into a more manage- able latent space, which they can We used the implementation provided by the authors of FlowRetrieval (Lin et al. 2024) for training the VAEs and computing the similarity for both, Flow and BR, in the LIBERO and OXE settings (https: /tt:::/giittt /tgggggtthuhooo. com/ otg.a//ooaooooggooooooooooooaaaa wwteee_rrriinnnncoee _ode).
compute similarities between prior and target data.
For both Flow and BR (and other heuristics such Action Retrieval (AR) and State Re- trieval (SR)), once each state in the prior data is assigned a score based on the similarity measure, we select the top x% of the data most similar the target where æ the selection budget.
STRAP (Memmel et al.) 2024 In the LIBERO experiments, we use the authors' STRAP implementation battps://iithii
to embed sub- trajectories with D2 (Oquab et al. 2023) and compute similarity via dynamic time warping. STRAP expects HDFF5formatted inputs. but our OXE pipeline relies on TFDS. We therefore adapted the STRAP code to accept TFDS datasets without altering its core logic.
While STRAP's original recommendation is to retrieve the top 100 sub-trajectories in LIBERO, we found that training Octo on these segments underperforms. Instead, we retrieve the most similar uub-trajectories until they constitute 10% of the prior data-matching the budget used by DataMIL and our other baselines. In LIBERO, this modification boosts success from 24.72% (with 100 segments) to 34.96% (our reported results) averaged over LIBERO-10 10 tasks. We apply the same retrieval strategy on OXE, sampling sub-trajectories until we match the selection size of our method and baselines.
For MetaWorld, we found it more effective to compute similarity over temporal windows rather than individual state-action pairs. Specifically, for each baseline (BR, SR, AR), we slide fixed- length horizon H over both prior and target data, concatenate each segment's states (and actions) into single high-dimensional vector, and then measure similarity between these flattened vectors. This horizon-based approach captures temporal context, enabling the baselines to reject noisy or suboptimal samples-ultimatel! ultimately improving retrieval quality and downstream policy performance. Our initial experiments found H 50 to perform best.
F TRAINING AND EVALUATION DETAILS
F.1 METAWORL.D
Dataset. The MetaWorld dataset is constructed from two sources: scripted expert policies and reinforcement learning (RL) exploration. MetaWorid provides scripted policies for each of its 50 tasks, which we use generate 4,000 episodes totaling 350K environment steps. For the RL data, we train multi-task SAC agent on all 50 tasks for 12 million transitions, reaching an average success rate of 21%.17 To create a representative prior dataset, we uniformly subsample from the SAC replay buffer across all tasks, yielding I million environment approxiaaallll 3x larger than the scripted data. For each target task, we generate expert demonstrations using the scripted policy as Diorget.
We cluster the prior dataset at the trajectory level and use 5 demonstrations from Dtarget to compute the proxy objective. We then compute the datamodels using the regression-bant baase datamodel estimator. The top 10% of prior trajectories, ranked by the datamodels, are selected to form D
Datamodel estimation using DataMIL.
Policy Training and Evaluation. We train a behavior cloning policy with an MLP backbone and tanh-squashed Gaussian output distribution from garage (garage contributors 2019) on Daet- In
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Table 2: Datasets utilized across the OXE subsets our experimental setup
Dataset
OXE13
OXE23
OXE24
Dataset
OXE13
OXE23
OXE24
RT1
CMU LAM Lab
Roboturk
Austin Buds
BC-Z
X
Austin Mutex
CMU Stretch
Austin Sailor
DIR Edan
X
Austin Sirius
Berkeley Autolab URS
Taco Play
Berkeley Fanuc
Jaco Play
Berkeley Cable
Hydra
Bridge
NYU Franka
NYU Door Opening
Forniture Bench
Toto
UCSD Kitehen
Kuka
X
X
preliminary experiments, found that co-training with Dtarget yielded negligible improvements, so we exclude in this setting. Each policy is trained and evaluated over random seeds.
F.2 LIBERO
Dataset. Our prior dataset consists of 4,500 human teleoperated demonstrations from LIBERO-90, with demonstrations per task. The 10 tasks from LIBERO-10 serve as our target tasks. For each, randomly sample 5 demonstrations to form the target dataset Diarget.
Datamodel estimation using DataMIL. Prior to running DataMIL, we segment the prior demon- strations into sub-trajen trajercccctoies of horizon length 15, which we found to provide good balance be- tween granularity and noise-robustness. We then estimate influence scores using the metagradient- based estimator with the weighted proxy metric described in Appendix [D.1 The top 10% of sub- trajectories, based on datamodel influence, are selected to form the selected dataset D.et-
Policy Training and Evaluation. We fine-tune language-conditioned Octo policy, starting from the publicly released Octo-small checkpoint, by co-training on Dtarget and 'Deet using a co-training ratio 0.5 for 10k steps. We evaluate each policy on the corresponding target task using 50 rollouts and report results averaged over 5 random seeds.
F.3 OXE
Table Task-wise experimental setup for selecting data and, training and evaluating policies
Embodiment
Prior Dataset
Prior Selection Ratio
No. of Target Demos
No. of Evaluations
Franka-Pick
Franka-Paes Pannda
OXE13
1%
14
Franka-Pouch
Franka-Panda
OXE23
0.75%
30
17
Tiago-Sink
Tiago
OXE24
0.5%
20
39
Mulii-Multitask
Franka-Panda
OXE24
1%
40 (total)
32 (total)
Drawer
10
10
Bread
15
12
Napkin
15
10
Dataset. We use subsets of the Open X-Embodiment (OXE) dataset (0 (e'leil al. 2024)
prior data. Specifically, we define three subsets OXE13, OXE23, and OXE24 with their respec- tive constituent datasets listed in Table The mapping between tasks and dataset subsets is shown in Table 3 For each task, we collect a separate target dataset via teleoperation (Dass et al. 2024). varying the number of demonstrations per task based on difficulty (see Table
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Datamodel estimation using DataMIL. Following the DataMIL recipe, cluster the prior data the trajectory level and estimate influence using the metagradient based estimator with our proposed proxy objective. To reduce distribution shift during datamodel training, halves: one half is used to compute the proxy metric, while the other is included in the training mix. After training, we select the top a% of prior trajectories based on influence scores, where is specified per task in Table 3
split D Darreet into two
Policy Training and Evaluation. We fine-tune a language-conditioned Octo-small checkpoint us- ing co-training on Drorget and the selected dataset Deet. with a co-training ratio = 0.5 for 50k steps. Final policy is evaluated on the corresponding target tasks using fixed number of real-world rollouts (Table To ensure fair comparisons, we fix the spatial configurations of relevant objects across all methods. For instance, in the Franka-Pouch task, we use 17 predefined object poses (position and orientation) for evaluation. In the more challenging Droid-Multitask setting, we re- port both full and partial successes as part of the final success rate (e.g., a partially closed drawer or grasping the bread/napkin), with partial completions weighted 0.5, All real-world evaluations were conducted using a single seed.
G COMPUTATION COSTS FOR DATA SELECTION
We report the computational costs associated with both estimators, measured across MetaWorld, LIBERO, and OXE.
Regression estimator. The regression estimator requires training and evaluating a policy on m random subsets of the dataset (Alg. [ While increasing m improves estimation quality, the com- putational cost is dominated by policy training, which scales linearly with m. In our MetaWorld experiments, we found m 10a sufficient obtain high-quality datamodels. This setting required a total of 8 GPU hours on 8 x A100 GPUs (equivalent to roughly 60-70 GPU hours on single GPU). Although this may appear expensive for single task, the same m trained policies can be reused to compute the proxy objective across all 50 tasks in the World suite, effectively amortizing the datamodel training cost to 1-2 hours per task.
Metagradient-based estimator. The metagradient-basel estimator is typically 3-5x slower than standard policy training, due to the additional cost of computing metagradients. Taking multiple steps (hyperparameteer 7' in Alg. 2 further improves estimation quality but incurring higher cost. In MetaWorld, data selection completes within ~~1 hour on single A5000 GPU. On LIBERO, parallel training across 4 A.500ss requires 16-20 hours comparable to the training times of VAE- based BR (Du et al.) 2023) and Flow (Lin et al. 2024) baselines. For OXE, which is substantially larger (300k trajectories), training takes 3-4 days on 4 A A100 GPUs. Notably, DataMII. avoids online rollouts, making training passive and able to run without any human oversight.
H HYPERPARAMETERS FOR ESTIMATING DATAMODELS
Below we provide the hyperparameters used for estimating the datamodels via regression and metagradient- based methods (refer to Alg. and 2 For experiments related details (e.g. amount of data selected), please refer to App.
Regression estimator. We utilize the regression estimator only in the MetaWorld setting. Specifi- cally, we construct training pairs by sampling datapoints with probability p = 0.1, training 400 x 3 ML. policy with ReLU activations and aanh-squashed Gaussian action head, and evaluating its loss on held-out dataset. This procedure is repeated 10a times to generate the training set used to regress the datamodel parameters.
Metagradient- based estimator. The hyperparameters used for the metagradient-basel estimator for each of the settings is listed in Table When we compute the metagradients, instead com- puting it over the entire training process (which would be too expensive), we compute it over only segment of training. Hence, the hyperparameter & refers to the number of steps at the end of training, over which the metagradients are computed.
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Table 4: Hyperparameters for the Metagradient-basce Estimator (Alg.
Hyperparameter
Metaworld
LIBERO
OXE
step size p
1.0
1.0
0.125
number of steps T policy architecture policy learner A(-) training steps metagradient steps optimizer Ir schedular
30
30
20
gaussian MLP
Octo-Small
Octo-Small
1100
2100
5100
100
100
100
adamw
adam cosine
adam
Icycle
cosine
LLM USAGE
We complied with the ICLR 2026 policies on LLM usage by using LLMs only polish the writing and improve clarity - correcting grammar, improving phrasing, and enhancing readability. We did use LLMs for generating original content, producing research ideas, analyzing data. Any substantive scientific content are the result of our own work, and we take full responsibility for them.
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Reviews:
[{"review_id": "6OivIXYh42", "reviewer": "Reviewer_DUws", "summary": "The paper tackles the challenge of improving task-specific robot imitation learning using large, diverse prior datasets that often contain helpful and harmful examples. Motivated by the limits of heuristic data filtering, which may select visually or semantically similar but suboptimal samples, the authors introduce DataMIL, a data-selection framework built on datamodels that estimates how each datapoint influences policy performance end-to-end. Their method avoids costly real-world rollouts by using a differentiable surrogate loss and meta-gradient or regression-based estimators to assign influence scores, selecting the most beneficial data while discarding harmful samples. Experiments across simulated and real manipulation tasks, including MetaWorld, LIBERO, and OXE, show consistent improvements over prior state-of-the-art retrieval and co-training baselines, particularly in heterogeneous real-world settings. The key takeaway is that performance-aware, end-to-end data attribution enables better curation of large robot datasets and significantly improves fine-tuning outcomes on new tasks.", "questions": "1. Did the authors analyze the outputs of DataMIL datamodel against some of the recent heuristic-based retrieval strategies for co-training? E.g. [1] and [2] have said that matching data distributions around specific factors such as camera poses and placement arrangements helps boost policy learning. According to [3], not all factors are equally important. Do those takeaways still hold for the datamodels trained in this paper?\n2. In line 408, the authors say that they “replace the All-Data baseline with a Random baseline” due to the scale of OXE. What exactly is the issue due to the scale of OXE? Does the policy underfit the entire data? In which case, what if the policy architecture is scaled up - is retrieval still necessary?\n\n\nReferences: \n\n[1] What Matters in Learning from Large-Scale Datasets for Robot Manipulation, Saxena et al., 2025\n\n[2] Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, Maddukuri et al., 2025\n\n[3] Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation, Xie et al., 2023", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "1. The paper builds on the datamodels paradigm and adapts it thoughtfully to robotics, incorporating principled techniques like metagradients and surrogate losses to avoid expensive rollouts.\n2. The paper demonstrates consistent improvements across 60+ simulation and real-world tasks (MetaWorld, LIBERO, OXE), making the empirical evidence strong and diverse.\n3. Comparisons against multiple state-of-the-art retrieval and co-training methods, plus ablations on surrogate loss vs true success metrics, meaningfully validate the approach.\n4. Demonstrates utility in real robot settings and across embodiments, an important step toward practical general-purpose robot learning.", "weaknesses": "1. Even with meta-gradients, building datamodels requires multiple policy trainings, making the pipeline significantly more expensive than standard fine-tuning or heuristic retrieval. This may limit usability in real-world labs without large compute.\n2. The method is evaluated primarily on one new task at a time. Broader deployment to large-scale continual or many-task adaptation scenarios remains underexplored.\n3. The core datamodel assumption is borrowed from NLP/CV; a deeper theoretical justification for robotic sequential decision-making settings would strengthen the contribution.\n4. I was deeply interested in looking at more qualitative data selection examples by DataMIL and considered baselines. While the authors provide some examples, I did not find them to be very convincing. It would be nice to see more examples along the lines of Figure 6 for Meta World. Moreover, retrieved demo examples shown for real-world tasks (Figure 8) also do not tell me much - why did DataMIL select those demos? It would be nice to include more discussion around qualitative results in the paper.\n5. While the paper compared against several retrieval methods in robotics datasets, simpler retrieval strategies, such as retrieving dataset with matching camera poses, objects being manipulated, etc., were not considered. Without such baselines, I believe it is hard to justify this method as the current best for retrieval-based learning from large-scale robotics datasets.\n\n\nOther minor issues:\n1. Caption in Figure 6 should be updated to show which task the plots are for.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "u5mXBZ6T1t", "reviewer": "Reviewer_PBcc", "summary": "This paper tackles the critical problem of selecting beneficial data from large, heterogeneous datasets for robot imitation learning. The authors argue that standard heuristic-based selection methods (e.g., visual or state-action similarity) are suboptimal and can even harm performance. To address this, the paper proposes DataMIL, a framework based on the 'datamodels' paradigm, which aims to predict a policy's performance based on the data it is trained on. The core contribution is a surrogate objective—the behavior cloning (BC) loss on a small set of target-task data. This surrogate avoids the need for expensive and risky real-world rollouts and is fully differentiable, enabling efficient metagradient-based estimation. The conclusion is supported by comprehensive experiments in simulation (MetaWorld, LIBERO) and on real-world hardware (OXE), demonstrating superior performance over several baselines.", "questions": "- Q1: Is there a trade-off strategy to efficiently identify the approach that yields the maximal performance improvement for the policy?\n- Q2: The authors find that the optimal clustering strategy differs between LIBERO (sub-trajectories) and OXE (full-trajectories). Could the authors provide a principled heuristic for selecting the appropriate clustering granularity based on dataset characteristics (e.g., dataset size, degree of heterogeneity, number of tasks)?\n- Q3: I'm curious about the potential impact of leveraging the identified \"negative samples\" explicitly during training, rather than only using positive examples. For instance, could a contrastive learning objective be designed to actively repel these negative examples while attracting positive ones? Intuitively, explicitly instructing the policy \"what not to learn\" might be more efficient or precise for defining the correct decision boundaries than only providing positive examples.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- The proposed problem is critical for the robot learning community. As the field increasingly relies on scaling up data (e.g., OXE), methods for effectively curating this data to specialize in new tasks are essential.\n- The paper's core idea of using a differentiable, rollout-free surrogate metric (BC loss on target data) is novel and effective. It elegantly adapts the datamodels framework to the specific constraints of robotics.\n- The evaluation is comprehensive, spanning multiple benchmarks (MetaWorld, LIBERO, OXE), policy architectures (MLP, Octo), and strong heuristic baselines (BR, Flow, STRAP).\n- The real-world evaluation on the heterogeneous OXE dataset is a significant strong point, especially the demonstration of successful cross-embodiment data selection (for the Tiago robot), which strongly supports the claims.", "weaknesses": "- The proposed data model incurs high computational costs, as it requires per-task estimation.\n- The method is far from \"plug-and-play,\" as it introduces numerous new hyperparameters that demand meticulous tuning, often relying on human heuristics and empirical expertise.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WW6Gsm9nZT", "reviewer": "Reviewer_FHqk", "summary": "The paper proposed a data selection strategy called DataMIL for robot imitation learning problems. DataMIL is built upon prior work (datamodels) on data selection in CV and NLP problems: the regression method and the metagradient method. To tackle the intractable datamodeling objective in robot imitation learning, the paper proposed some modifications to the standard datamodels: (1) a proxy metric, (2) clustering demonstrations temporally, and (3) a strategy to prevent distribution shift. Empirically, the DataMIL achieved higher success rates on both simulated and real-world multi-task robotics benchmarks.", "questions": "- Fig 2: Is the target-only policy trained on a dataset with expert and sub-optimal demonstrations on the target task? I am curious about the performance of imitation learning only on target expert demonstrations. \n\n- The ablation experiments for the proxy metric $\\hat{M}$ in Sec 4.2 only limits to a single task.\n\n- The “clustering training examples” paragraph in Sec. 4.2: The description of the specific clustering methods are not clear from the texts. Is there any ablation study showing the effects and necessity of clustering training examples?\n\n- Fig 2, Fig 3, Fig 4, and Sec 5.2: Is there a direct quantitative comparison between the dataset selected by different methods? For example, if imitation learning on the target expert data can achieve almost 100% success rate, then computing the IoU score or some clustering based scores (e.g., NMI score) between these expert data and data selected by different methods would be direct comparisons.\n\n- Any ideas about how to perform data selection only on fine-tuning datasets and how to reduce the distribution shift if we cannot retain the prior dataset?\n\n- Any comments on computing the metric as in Eq.5 if the policy objective itself is not differentiable, e.g., if we were doing RL instead of IL.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Instead of using heuristics such as visual similarity or state–action closeness to select subsets of prior data, this paper proposed to use the performance-aware datamodels to predict scores for selecting each data, resulting in an end-to-end data selection algorithm for robotics imitation learning problem.\n\n- The proposed proxy metric prevents expensive and intractable rollouts on real-robot during data selection and also prevents repeatedly learning the same algorithms on different subsets of the prior data.\n\n- Empirically, the evaluations of the proposed methods range from both simulated and real-world robotics benchmarks and show consistent gains of DataMIL over multiple baselines.", "weaknesses": "- Gradient-based data reweighting and data balancing for supervised learning problems has been widely studied in the literature [1, 2, 3]. The concepts of datamodels and regression based methods have been proposed in prior methods. Also, the data selection for robotics imitation learning is still a supervised learning problem. Overall, the contributions of the paper seem to be incremental.\n\n- The explanations for some strategies in Sec. 4.2 are unclear and some ablation experiments are limited. See the question part for details.\n\n- Since the paper focuses on empirical studies, including preliminary open-source code in the submission would strengthen the conclusions.\n\n[1] Ren, Mengye, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. \"Learning to reweight examples for robust deep learning.\" In International conference on machine learning, pp. 4334-4343. PMLR, 2018.\n\n[2] Shu, Jun, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. \"Meta-weight-net: Learning an explicit mapping for sample weighting.\" Advances in neural information processing systems 32 (2019).\n\n[3] Wang, X., Pham, H., Michel, P., Anastasopoulos, A., Carbonell, J., & Neubig, G. (2020, November). Optimizing data usage via differentiable rewards. In International Conference on Machine Learning (pp. 9983-9995). PMLR.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gxC8JcEulo", "reviewer": "Reviewer_FrUR", "summary": "This paper introduces DataMIL, a framework for selecting the most beneficial data from large, heterogeneous prior datasets to improve imitation learning for specific robotic tasks. The core problem addressed is that naively co-training with large datasets can harm performance, while heuristic-based selection methods (e.g., based on visual or semantic similarity) are often suboptimal because they don't directly consider the data's impact on the final policy's performance.\n\n\nDataMIL adapts the \"datamodels\" paradigm to robotics. It works by estimating the influence of each data point (or cluster of data points) on a policy's task success. To make this feasible for robotics, it introduces two key adaptations:\n\n\n1. A surrogate objective: Instead of using expensive real-world rollouts to measure performance during data selection, DataMIL uses the policy's validation loss on a small set of target-task data as a differentiable proxy metric.\n\n2. Efficient estimators: It explores two methods for calculating data influence scores: a comprehensive but computationally expensive Regression Estimator and a more efficient Metagradient-based Estimator suitable for large, modern policies like Octo.\n\n\nThe proposed pipeline involves clustering prior data, estimating influence scores for each cluster using the surrogate objective, selecting the top-ranked clusters, and finally co-training a policy on this selected data and the target task data. The authors validate DataMIL across 60+ simulation and real-world tasks, demonstrating superior performance over several baselines by selecting data from large datasets like MetaWorld, LIBERO-90, and the Open X-Embodiment (OXE) dataset.", "questions": "While the validation loss proxy appears to work well in your experiments, its reliability is a critical assumption. Can you characterize the potential failure modes? In what specific types of robotic tasks (e.g., those with high physical variability, tasks requiring tool use where initial states are similar but require vastly different actions, or long-horizon tasks with sparse success signals) might minimizing validation loss lead to the selection of data that is detrimental to real-world task success?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- As robotic datasets like OXE and DROID grow in scale and heterogeneity, the problem of \"negative transfer\" or performance degradation from irrelevant data becomes increasingly severe. DataMIL provides a concrete solution to this important and timely challenge in the field.\n- The paper's primary strength is its shift from heuristic-based data selection to a more principled, end-to-end optimization. By directly estimating a data point's influence on policy performance (via a proxy), DataMIL moves beyond potentially misleading similarity metrics and focuses on the ultimate goal: task success. This is a significant conceptual improvement over methods that rely purely on visual, motion, or state-action similarity.\n- The paper successfully adapts a powerful idea from the broader machine learning community to the specific constraints of robotics. The introduction of a differentiable, rollout-free surrogate loss is a clever and necessary innovation that makes the datamodels framework tractable for physical systems.\n- The authors provide comprehensive validation across a wide range of settings: a large number of simulation tasks in MetaWorld (50) and LIBERO (10), and challenging real-world tasks using the massive and heterogeneous OXE dataset. The demonstrated ability to select useful data for an unseen embodiment (Tiago robot) is particularly compelling.", "weaknesses": "- The entire method hinges on the assumption that minimizing validation loss on a small target dataset ($\\hat{\\mathcal{M}}$) is a reliable proxy for maximizing real-world success rate ($\\mathcal{M}$). The paper provides one experiment (Fig. 2) to show this correlation holds, but this is on a single task. This proxy is known to be noisy in robotics. It is plausible that for tasks involving complex contact dynamics, deceptive local minima, or sparse rewards, data that merely helps \"fit\" the target demonstrations (i.e., lower BC loss) might not be the data that promotes robust, generalizable behavior. The proxy might favor data that is visually simple or has similar action patterns, even if other data could teach a more valuable physical prior, potentially filtering out more diverse or challenging data from the prior dataset that could have been crucial for improving robustness and generalization.\n- The method introduces several critical hyperparameters that significantly impact performance, and the paper relies on empirical ablations to set them.\n1. Figure 10a shows that performance is sensitive to the percentage of data selected. There is no principled way offered to determine the optimal ratio a priori, meaning users must perform a costly sweep.\n\n2. Figure 10b demonstrates that performance varies with cluster granularity. The justification that the optimal strategy is a \"function of the dataset size\" is intuitive but not actionable without further guidance. \n\n3. The paper fixes α=0.5 based on simulation results (Fig. 10c), but this optimal ratio is likely task- and dataset-dependent. The need for these expensive, nested hyperparameter sweeps undermines the overall efficiency of the framework.\n\n- The cross-embodiment selection for the Tiago robot (Fig. 5a) is interesting, but the paper only observes that the selected data represents \"robots operating on a table top from an ego-perspective\". This is a very general description. It's unclear if DataMIL is discovering abstract skills or simply matching on coarse visual scene structure, the latter being a much weaker form of transfer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)