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arxiv: 2606.06418 · v1 · pith:OLXPA3T2new · submitted 2026-06-04 · 💻 cs.LG · cs.AI· cs.SY· eess.SY

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss

Thomas T. Zhang , Alok Shah , Yifei Zhang , Vincent Zhang , Nikolai Matni , Max Simchowitz This is my paper

Pith reviewed 2026-06-28 02:31 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.SYeess.SY
keywords double-preconditioningDoPrtest-time feedbackTTFactivation-wise preconditioningdownstream performancevalidation loss mismatchneural network optimization
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The pith

Adding activation-wise preconditioning to standard optimizers improves downstream performance in test-time feedback settings even when validation loss stays flat.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces double-preconditioning (DoPr) for neural networks trained on one-step prediction losses yet deployed through iterative rollouts of their own outputs. In these test-time feedback settings the training objective fails to capture error accumulation that degrades long-horizon metrics such as task success or generation quality. DoPr augments gradient-wise preconditioners like Adam with activation-wise preconditioning drawn from methods such as KFAC. The authors present evidence that this addition functions as a drop-in change that raises downstream metrics across language modeling, flow-based generation, and robot policy tasks. The gains frequently appear without corresponding reductions in the validation loss, which prompts reconsideration of how models trained under one-step supervision should be evaluated.

Core claim

We introduce a new optimization paradigm called double-preconditioning (DoPr) that combines gradient-wise preconditioning with activation-wise preconditioning. We show that the addition of AP yields a drop-in intervention for increasing downstream model performance across a range of TTF settings. Interestingly, these gains in test-time performance do not consistently accompany improvements in validation loss, opening new questions about how to properly evaluate models trained with one-step supervised objectives.

What carries the argument

Double-preconditioning (DoPr), which augments gradient-wise preconditioners with activation-wise preconditioning (AP) such as KFAC to target error accumulation during test-time rollouts.

If this is right

  • DoPr can be inserted into existing training pipelines for autoregressive language models, flow-based generators, and robot policies without altering the loss or architecture.
  • Downstream metrics such as task success rate and generation quality rise while the one-step validation loss often remains unchanged.
  • Optimization choices become a distinct lever for mitigating train-test mismatch in sequential deployment, alongside data curation and objective design.
  • Evaluation protocols for one-step supervised models should track rollout quality separately from validation loss.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standard validation loss may be an incomplete signal for model selection when deployment involves long iterative predictions.
  • Activation-wise statistics appear to encode information that stabilizes iterative outputs beyond what gradient preconditioning alone captures.
  • The method could be tested on longer rollout horizons or on tasks with explicit compounding noise to measure how far the benefit extends.

Load-bearing premise

The reported gains in downstream metrics are caused by the addition of activation-wise preconditioning rather than by differences in hyperparameter tuning, implementation details, or dataset-specific effects.

What would settle it

A controlled comparison in which the only change is the addition of activation-wise preconditioning, with all other hyperparameters and code paths held fixed, shows no consistent lift in downstream TTF metrics across multiple tasks.

Figures

Figures reproduced from arXiv: 2606.06418 by Alok Shah, Max Simchowitz, Nikolai Matni, Thomas T. Zhang, Vincent Zhang, Yifei Zhang.

Figure 1
Figure 1. Figure 1: Standard optimizers, while effective at accelerating validation loss convergence, may induce poor feature learning. This can exacerbate distribution shift due to test-time feedback (TTF), the growing compounding errors as the model is deployed along its own predictions, ultimately leading to degraded downstream performance. We propose Double Preconditioning (DoPr) as a plug-in approach, where we apply a pa… view at source ↗
Figure 2
Figure 2. Figure 2: Many settings involve using per-step supervised objectives along training sequences. However, due to rolling out along the model’s own predictions, mismatches between directions salient for Lval(πθ ) versus Rtest(πθ ) cause TTF shift. Hypothetically, instead optimizing for directions salient for Lideal(πθ )—often unavailable for offline training—would induce smaller TTF shift. Optimizer Preconditioning for… view at source ↗
Figure 3
Figure 3. Figure 3: A depiction of how test-time feedback exacerbates distribution shift: errors in the network’s predictions affect the ensuing states, which changes their distribution away from the one seen in training. Changes in the state distribution (red) also affect the quality of the learned features (depicted as blue, to purple, to red) at intermediate layers (e.g., Proposition 3.2). Errors propagate layerwise, furth… view at source ↗
Figure 4
Figure 4. Figure 4: Mismatch between validation loss and feature learning. In-distribution validation loss converges and is even accelerated by GP. However, unless AP is applied, the feature subspace distance (3.4) converges poorly, which can exacerbate TTF (Proposition 3.2). Details in Appendix C.2. is ill conditioned [Collins et al., 2021, Zhang et al., 2024]. Taken together, these findings imply that AP optimizers have the… view at source ↗
Figure 5
Figure 5. Figure 5: When an affine transform is applied to the input distribution, with the initial weights transformed accordingly (4.3), the SGD trajectories (left) diverge, while the DoPr-SGD trajectories (right) match exactly, demonstrating the invariance induced by DoPr under affine transforms (Proposition 4.2). See Appendix C.3 for experiment details. reveals that after a step of (full-batch) gradient descent: W+ = W − … view at source ↗
Figure 6
Figure 6. Figure 6: µP scaling behavior. Left: DoPr-AdamW’s scaling trends under standard (SP) and AdamW’s µP pa￾rameterizations on a GPT2 model. We find base AdamW’s µP-scaling also enables hyperparameter transfer for DoPr-AdamW. Right: update-to-weight norm ratio scaling trend, under standard constant weight decay (SP) and AdamW’s weight decay µP scaling. See Appendix C.4 for full details. Remark 4.2. Isotropic activations … view at source ↗
Figure 7
Figure 7. Figure 7: Humanoid-v5 DoPr performance across AdamW, Muon, Signum, and AdaMuon. DoPr variants attain higher terminal reward which does not consistently correlate with train or validation loss improvements. 5 Capabilities In Section 4, we derive DoPr to address the poor feature learning of standard optimizers in TTF settings, with the promise of inducing better downstream behavior. To demonstrate this, we run experim… view at source ↗
Figure 8
Figure 8. Figure 8: Tool Hang (PH) and Transport (PH) Best Success Rate for AdamW, Muon, and DoPr variants. Each curve shows the min/median/max over 3 random seeds. DoPr-variants outperform their baselines. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: GSM8K 3B SFT sweep. Peak GSM8K accuracy across training steps vs. learning rate, comparing AdamW with DoPr-AdamW. The comparison between Muon and DoPr-Muon is provided in Appendix C.7.1. 5.2 Image-Based Robot Policy Learning We evaluate DoPr on pixel-based imitation learning with generative policies on Robomimic tasks. We focus on challenging tasks Tool-Hang Proficient-Human (PH) and Transport (PH), which … view at source ↗
Figure 10
Figure 10. Figure 10: 8B SFT sweep. We plot final accuracy on GSM8K, GSM8K-CoT, and MATH-500 against validation loss (NLL), computed on a 10K held-out subset of the training data. Each point is a final checkpoint from the learning-rate sweep while label numbers denote index of learning rates sorted by size: 2e-5, 5e-5, 7e-5, 1e-4, 2e-4, 5e-4, 7e-4. Error bars indicate one standard error. We lastly remark additional experiments… view at source ↗
Figure 11
Figure 11. Figure 11: We train a 3-layer MLP on CIFAR, and sweep over learning rates. We compare Standard Parametrization (SP, left) and Maximal Update parametrization (µP, right). The final train loss is reported for both µP and SP. µP achieves learning rate transfer as width increases. Models and datasets. In [PITH_FULL_IMAGE:figures/full_fig_p045_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: µP scaling behavior. Learning-rate sweeps comparing for DoPr-Muon under non-µP(SP) and µP parameterizations on GPT2 language model. D = Muon(M) And so, the update is W+ = W − ηD We note that Muon orthonormalizes the gradient matrix (in this case M), up to numerical errors in the Newton-Schulz iterations. Thus, we may treat D as orthonormal. Then, we get: W+z = Wz − ηDz The preconditioning only affects the… view at source ↗
Figure 13
Figure 13. Figure 13: Half-Cheetah-v5 DoPr performance across AdamW, Muon, Signum, and AdaMuon. DoPr variants attain higher terminal reward which does not consistently correlate with train or validation loss improvements. C.5 State-based Imitation Learning This section provides implementation details and supporting results for Humanoid-v5 and Half-Cheetah-v5 Gymnasium benchmarks [Towers et al., 2025] Model and architecture. To… view at source ↗
Figure 14
Figure 14. Figure 14: Best mean success rate throughout training on Tool-Hang and Transport tasks from robomimic. We compare AdamW, Muon, and their DoPr variants, reporting the best mean success rate over evaluation checkpoints every 20k steps. Evaluation protocol. Evaluation is performed with lm-evaluation-harness using the vLLM back￾end. Fine-tuned models are evaluated by loading the frozen Llama-3.1-8B base weights together… view at source ↗
Figure 15
Figure 15. Figure 15: Training Loss on Tool-Hang and Transport tasks from robomimic. We compare AdamW, Muon, and their DoPr variants trajectory. As in flow matching, the model minimizes errors on (xt , yt ), where yt = v(xt , t) is the closed-form target velocity. At generation time, however, samples are produced by iteratively integrating the learned field, e.g. d dt bzt = bv(bxt , t), so velocity errors can accumulate and sh… view at source ↗
Figure 16
Figure 16. Figure 16: GSM8K 3B SFT sweep. Peak GSM8K accuracy across training steps vs. learning rate, comparing between Muon and DoPr-Muon. improvements in train loss convergence. We hypothesize that generative modeling is a relatively unique TTF setting, in that one has control over the data-generating dynamical system. For example, many works study “straightening” the transport trajectory (cf. standard flow matching versus … view at source ↗
Figure 17
Figure 17. Figure 17: SiT-S on ImageNet256. Top row: perceptual quality (FID-50K) vs. training steps. Bottom row: training loss trajectories. We see that DoPr improves sample quality as well as loss convergence. AdamW (20K steps) DoPr-AdamW (20K steps) [PITH_FULL_IMAGE:figures/full_fig_p060_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Generated ImageNet256 samples at 20K training steps (SiT-S, [PITH_FULL_IMAGE:figures/full_fig_p060_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: We train 3 MLPs on CIFAR, and a nonlinear invertible transform of CIFAR. We plot the validation loss for the 3 models for each of AdamW (left), SGD (middle), and Muon (right). Across all 3, it can be seen that the Grafted model’s trajectory is closer to the Transformed model than the Original model. 0.0 0.6 1.2 1.8 2.4 3.0 ratio Muon DoPr-Muon k_proj:B o_proj:B o_proj:A gate_proj:B up_proj:B k_proj:A q_pr… view at source ↗
Figure 20
Figure 20. Figure 20: Metric values aggregated by layer group for Muon and DoPr-Muon. The bottom panel shows the mean difference (DoPr-Muon minus Muon). 61 [PITH_FULL_IMAGE:figures/full_fig_p061_20.png] view at source ↗
read the original abstract

Many modern applications of deep learning involve training a neural network via a one-step prediction loss (e.g., $L^2$ regression, cross-entropy), but deploy the network by rolling out along its own predictions. Key examples include autoregressive language modeling, flow-based generative modeling, and robot policy learning. It is well-documented that these settings induce a phenomenon we call test-time feedback (TTF): the mismatch between the training/validation loss and downstream metrics of interest, such as task success rate and generation quality, which grows with task length. While data curation, architecture, and objective design have been proposed to combat train-test shift in TTF settings, this paper proposes optimization as a new design axis to mitigate error accumulation. Specifically, we introduce a new optimization paradigm called double-preconditioning (DoPr) uniquely tailored to the challenges of TTF. DoPr combines gradient-wise preconditioning, as in Adam and Muon, with activation-wise preconditioning (AP), such as in KFAC. We show that the addition of AP yields a drop-in intervention for increasing downstream model performance across a range of TTF settings. Interestingly, these gains in test-time performance do not consistently accompany improvements in validation loss, opening new questions about how to properly evaluate models trained with one-step supervised objectives.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces double preconditioning (DoPr), which combines gradient-wise preconditioning (e.g., Adam, Muon) with activation-wise preconditioning (AP, e.g., KFAC), as a method to improve downstream performance in test-time feedback (TTF) settings like autoregressive language modeling, flow-based generative modeling, and robot policy learning. It claims that adding AP acts as a drop-in intervention to boost task success rate and generation quality, even when validation loss does not improve, suggesting optimization as a new design axis beyond data, architecture, and objectives.

Significance. If the empirical gains are robustly demonstrated and attributable to the activation-wise component, this work would be significant for providing an optimization-based approach to mitigate error accumulation in TTF settings. It would also highlight limitations in using validation loss for evaluating models trained with one-step objectives, potentially influencing training practices in sequential prediction tasks.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'the addition of AP yields a drop-in intervention for increasing downstream model performance across a range of TTF settings' is presented without any experiment details, baselines, statistical tests, ablation results, or controls for hyperparameter tuning effort. This makes it impossible to assess whether the gains are caused by AP or by unequal tuning budgets or implementation differences.
  2. [Abstract] Abstract: No specific TTF settings, models, datasets, quantitative results, or matched experimental protocols are described, preventing evaluation of the generality and magnitude of the claimed improvements or isolation of the AP effect from confounding factors.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. The two major comments both concern the level of detail in the abstract. We agree that abstracts are high-level by design and will revise the abstract to incorporate more specifics on settings, results, and controls while preserving its brevity. Full experimental details, ablations, and protocols remain in the body of the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'the addition of AP yields a drop-in intervention for increasing downstream model performance across a range of TTF settings' is presented without any experiment details, baselines, statistical tests, ablation results, or controls for hyperparameter tuning effort. This makes it impossible to assess whether the gains are caused by AP or by unequal tuning budgets or implementation differences.

    Authors: We acknowledge the abstract's conciseness omits these elements. The manuscript reports experiments across autoregressive language modeling, flow-based generative modeling, and robot policy learning, with direct comparisons to gradient-wise preconditioners (Adam, Muon) and ablations isolating the activation-wise component. Hyperparameter search budgets were matched across conditions where feasible, and results include task-success metrics with variability estimates. We will expand the abstract with one or two sentences summarizing the TTF settings and the observed dissociation between validation loss and downstream performance. revision: yes

  2. Referee: [Abstract] Abstract: No specific TTF settings, models, datasets, quantitative results, or matched experimental protocols are described, preventing evaluation of the generality and magnitude of the claimed improvements or isolation of the AP effect from confounding factors.

    Authors: The abstract intentionally remains high-level. Sections 4–5 of the manuscript specify the models (transformers, flow networks, policy networks), datasets, rollout lengths, and evaluation protocols, including controls that hold optimizer hyperparameters fixed except for the addition of the activation-wise preconditioner. We will revise the abstract to name the three TTF domains and note that improvements appear in downstream metrics even when validation loss is comparable. revision: yes

Circularity Check

0 steps flagged

No circularity; purely empirical claim with no derivation chain

full rationale

The paper presents no equations, derivations, or first-principles results. Its central claim—that adding activation-wise preconditioning (AP) improves downstream TTF metrics—is supported solely by experimental observations, not by any fitted parameter, self-referential definition, or self-citation chain that reduces the result to its inputs. Citations to Adam, Muon, and KFAC are to external prior work and do not bear the load of the empirical finding. The result is therefore self-contained against external benchmarks and receives the default non-circularity outcome.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no mathematical derivations, fitted constants, or new postulated entities; the claim rests entirely on an empirical intervention whose details are not supplied.

pith-pipeline@v0.9.1-grok · 5787 in / 961 out tokens · 36520 ms · 2026-06-28T02:31:38.127446+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

300 extracted references · 2 canonical work pages

  1. [1]

    2004 , publisher=

    Optimal control theory: an introduction , author=. 2004 , publisher=

    2004

  2. [2]

    Advances in Neural Information Processing Systems , volume=

    TaSIL: Taylor series imitation learning , author=. Advances in Neural Information Processing Systems , volume=

  3. [3]

    International Conference on Machine Learning , pages=

    Information-theoretic considerations in batch reinforcement learning , author=. International Conference on Machine Learning , pages=. 2019 , organization=

    2019

  4. [4]

    SIAM journal on control and optimization , volume=

    A Lyapunov-like characterization of asymptotic controllability , author=. SIAM journal on control and optimization , volume=. 1983 , publisher=

    1983

  5. [5]

    arXiv preprint arXiv:2404.14367 , year=

    Preference fine-tuning of llms should leverage suboptimal, on-policy data , author=. arXiv preprint arXiv:2404.14367 , year=

    arXiv

  6. [6]

    2025 , eprint=

    Gymnasium: A Standard Interface for Reinforcement Learning Environments , author=. 2025 , eprint=

    2025

  7. [7]

    arXiv preprint arXiv:1711.05101 , year=

    Decoupled weight decay regularization , author=. arXiv preprint arXiv:1711.05101 , year=

    Pith/arXiv arXiv

  8. [8]

    arXiv preprint arXiv:1608.03983 , year=

    SGDR: Stochastic gradient descent with warm restarts , author=. arXiv preprint arXiv:1608.03983 , year=

    Pith/arXiv arXiv

  9. [9]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Film: Visual reasoning with a general conditioning layer , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  10. [10]

    arXiv preprint arXiv:2306.06253 , year=

    Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models , author=. arXiv preprint arXiv:2306.06253 , year=

    arXiv

  11. [11]

    arXiv preprint arXiv:2309.08587 , year=

    Compositional Foundation Models for Hierarchical Planning , author=. arXiv preprint arXiv:2309.08587 , year=

    arXiv

  12. [12]

    arXiv preprint arXiv:2205.09991 , year=

    Planning with diffusion for flexible behavior synthesis , author=. arXiv preprint arXiv:2205.09991 , year=

    Pith/arXiv arXiv

  13. [13]

    arXiv preprint arXiv:2301.10677 , year=

    Imitating human behaviour with diffusion models , author=. arXiv preprint arXiv:2301.10677 , year=

    arXiv

  14. [14]

    arXiv preprint arXiv:2211.15657 , year=

    Is Conditional Generative Modeling all you need for Decision-Making? , author=. arXiv preprint arXiv:2211.15657 , year=

    Pith/arXiv arXiv

  15. [15]

    arXiv preprint arXiv:2303.04137 , year=

    Diffusion Policy: Visuomotor Policy Learning via Action Diffusion , author=. arXiv preprint arXiv:2303.04137 , year=

    Pith/arXiv arXiv

  16. [16]

    arXiv preprint arXiv:2304.13705 , year=

    Learning fine-grained bimanual manipulation with low-cost hardware , author=. arXiv preprint arXiv:2304.13705 , year=

    Pith/arXiv arXiv

  17. [17]

    Advances in neural information processing systems , volume=

    Behavior Transformers: Cloning k modes with one stone , author=. Advances in neural information processing systems , volume=

  18. [18]

    Advances in neural information processing systems , volume=

    Decision transformer: Reinforcement learning via sequence modeling , author=. Advances in neural information processing systems , volume=

  19. [19]

    Neural computing and applications , volume=

    Deep imitation learning for 3D navigation tasks , author=. Neural computing and applications , volume=. 2018 , publisher=

    2018

  20. [20]

    ACM Computing Surveys (CSUR) , volume=

    Imitation learning: A survey of learning methods , author=. ACM Computing Surveys (CSUR) , volume=. 2017 , publisher=

    2017

  21. [21]

    2015 , eprint=

    Deep Residual Learning for Image Recognition , author=. 2015 , eprint=

    2015

  22. [22]

    Nature , volume=

    Champion-level drone racing using deep reinforcement learning , author=. Nature , volume=. 2023 , publisher=

    2023

  23. [23]

    2018 , eprint=

    Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , author=. 2018 , eprint=

    2018

  24. [24]

    Keller Jordan , title =

  25. [25]

    2024 , eprint=

    Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning , author=. 2024 , eprint=

    2024

  26. [26]

    2024 , eprint=

    Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training , author=. 2024 , eprint=

    2024

  27. [27]

    2024 , url =

    Keller Jordan and Jeremy Bernstein and Brendan Rappazzo and @fernbear.bsky.social and Boza Vlado and You Jiacheng and Franz Cesista and Braden Koszarsky and @Grad62304977 , title =. 2024 , url =

    2024

  28. [28]

    2024 , eprint=

    The Ingredients for Robotic Diffusion Transformers , author=. 2024 , eprint=

    2024

  29. [29]

    arXiv preprint arXiv:2108.03298 , year=

    What Matters in Learning from Offline Human Demonstrations for Robot Manipulation , author=. arXiv preprint arXiv:2108.03298 , year=

    Pith/arXiv arXiv

  30. [30]

    2017 , eprint=

    Proximal Policy Optimization Algorithms , author=. 2017 , eprint=

    2017

  31. [31]

    2023 , eprint=

    Symbolic Discovery of Optimization Algorithms , author=. 2023 , eprint=

    2023

  32. [32]

    arXiv preprint arXiv:1604.07316 , year=

    End to end learning for self-driving cars , author=. arXiv preprint arXiv:1604.07316 , year=

    Pith/arXiv arXiv

  33. [33]

    Conference on robot learning , pages=

    One-shot visual imitation learning via meta-learning , author=. Conference on robot learning , pages=. 2017 , organization=

    2017

  34. [34]

    2018 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Deep imitation learning for complex manipulation tasks from virtual reality teleoperation , author=. 2018 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2018 , organization=

    2018

  35. [35]

    arXiv preprint arXiv:1812.03079 , year=

    Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst , author=. arXiv preprint arXiv:1812.03079 , year=

    Pith/arXiv arXiv

  36. [36]

    Learning for Dynamics and Control Conference , pages=

    On the sample complexity of stability constrained imitation learning , author=. Learning for Dynamics and Control Conference , pages=. 2022 , organization=

    2022

  37. [37]

    Proceedings of the fourteenth international conference on artificial intelligence and statistics , pages=

    A reduction of imitation learning and structured prediction to no-regret online learning , author=. Proceedings of the fourteenth international conference on artificial intelligence and statistics , pages=. 2011 , organization=

    2011

  38. [38]

    Conference on robot learning , pages=

    Dart: Noise injection for robust imitation learning , author=. Conference on robot learning , pages=. 2017 , organization=

    2017

  39. [39]

    2019 International Conference on Robotics and Automation (ICRA) , pages=

    Hg-dagger: Interactive imitation learning with human experts , author=. 2019 International Conference on Robotics and Automation (ICRA) , pages=. 2019 , organization=

    2019

  40. [40]

    arXiv preprint arXiv:2303.00638 , year=

    MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts , author=. arXiv preprint arXiv:2303.00638 , year=

    arXiv

  41. [41]

    arXiv preprint arXiv:2106.03207 , year=

    Mitigating covariate shift in imitation learning via offline data without great coverage , author=. arXiv preprint arXiv:2106.03207 , year=

    arXiv

  42. [42]

    Advances in Neural Information Processing Systems , volume=

    Causal confusion in imitation learning , author=. Advances in Neural Information Processing Systems , volume=

  43. [43]

    Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=

    Efficient reductions for imitation learning , author=. Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=. 2010 , organization=

    2010

  44. [44]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  45. [45]

    2003 , publisher=

    On the sample complexity of reinforcement learning , author=. 2003 , publisher=

    2003

  46. [46]

    Distributional and L^q norm inequalities for polynomials over convex bodies in

    Carbery, Anthony and Wright, James , journal=. Distributional and L^q norm inequalities for polynomials over convex bodies in. 2001 , publisher=

    2001

  47. [47]

    International conference on machine learning , pages=

    How to escape saddle points efficiently , author=. International conference on machine learning , pages=. 2017 , organization=

    2017

  48. [48]

    Mathematics of Computation , volume=

    Recovery of Sobolev functions restricted to iid sampling , author=. Mathematics of Computation , volume=

  49. [49]

    arXiv preprint cs/0408007 , year=

    Online convex optimization in the bandit setting: gradient descent without a gradient , author=. arXiv preprint cs/0408007 , year=

    Pith/arXiv arXiv

  50. [50]

    Nonlinear and optimal control theory: lectures given at the CIME summer school held in Cetraro, Italy June 19--29, 2004 , pages=

    Input to state stability: Basic concepts and results , author=. Nonlinear and optimal control theory: lectures given at the CIME summer school held in Cetraro, Italy June 19--29, 2004 , pages=. 2008 , publisher=

    2004

  51. [51]

    Statistics & Probability Letters , volume=

    Optimal global rates of convergence for interpolation problems with random design , author=. Statistics & Probability Letters , volume=. 2013 , publisher=

    2013

  52. [52]

    arXiv preprint arXiv:2406.13447 , year=

    High-probability minimax lower bounds , author=. arXiv preprint arXiv:2406.13447 , year=

    arXiv

  53. [53]

    2006 , publisher=

    A distribution-free theory of nonparametric regression , author=. 2006 , publisher=

    2006

  54. [54]

    Advances in Neural Information Processing Systems , year=

    Provable guarantees for generative behavior cloning: Bridging low-level stability and high-level behavior , author=. Advances in Neural Information Processing Systems , year=

  55. [55]

    IFAC Proceedings Volumes , volume=

    Uncertainty in unstable systems: the gap metric , author=. IFAC Proceedings Volumes , volume=. 1981 , publisher=

    1981

  56. [56]

    2000 , publisher=

    Empirical Processes in M-estimation , author=. 2000 , publisher=

    2000

  57. [57]

    2019 , publisher=

    High-dimensional statistics: A non-asymptotic viewpoint , author=. 2019 , publisher=

    2019

  58. [58]

    The Thirty Seventh Annual Conference on Learning Theory , pages=

    Minimax Linear Regression under the Quantile Risk , author=. The Thirty Seventh Annual Conference on Learning Theory , pages=. 2024 , organization=

    2024

  59. [59]

    Proceedings of The 7th Conference on Robot Learning , pages =

    RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control , author =. Proceedings of The 7th Conference on Robot Learning , pages =. 2023 , editor =

    2023

  60. [60]

    Advances in Neural Information Processing Systems , volume=

    Conservative q-learning for offline reinforcement learning , author=. Advances in Neural Information Processing Systems , volume=

  61. [61]

    arXiv preprint arXiv:2110.06169 , year=

    Offline reinforcement learning with implicit q-learning , author=. arXiv preprint arXiv:2110.06169 , year=

    Pith/arXiv arXiv

  62. [62]

    Advances in neural information processing systems , volume=

    Generative adversarial imitation learning , author=. Advances in neural information processing systems , volume=

  63. [63]

    International Conference on Machine Learning , pages=

    Of moments and matching: A game-theoretic framework for closing the imitation gap , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  64. [64]

    arXiv preprint arXiv:2410.13855 , year=

    Diffusing States and Matching Scores: A New Framework for Imitation Learning , author=. arXiv preprint arXiv:2410.13855 , year=

    arXiv

  65. [65]

    arXiv preprint arXiv:2410.24164 , year=

    pi_0 : A Vision-Language-Action Flow Model for General Robot Control , author=. arXiv preprint arXiv:2410.24164 , year=

    Pith/arXiv arXiv

  66. [66]

    arXiv preprint arXiv:2304.10573 , year=

    Idql: Implicit q-learning as an actor-critic method with diffusion policies , author=. arXiv preprint arXiv:2304.10573 , year=

    Pith/arXiv arXiv

  67. [67]

    The Annals of Statistics , volume=

    On nonparametric estimation of density level sets , author=. The Annals of Statistics , volume=. 1997 , publisher=

    1997

  68. [68]

    Journal of Multivariate Analysis , volume=

    Nonparametric estimation of a function from noiseless observations at random points , author=. Journal of Multivariate Analysis , volume=. 2017 , publisher=

    2017

  69. [69]

    2018 , publisher=

    High-dimensional probability: An introduction with applications in data science , author=. 2018 , publisher=

    2018

  70. [70]

    IEEE Transactions on Intelligent Vehicles , volume=

    Motion planning for autonomous driving: The state of the art and future perspectives , author=. IEEE Transactions on Intelligent Vehicles , volume=. 2023 , publisher=

    2023

  71. [71]

    2021 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Grasping with chopsticks: Combating covariate shift in model-free imitation learning for fine manipulation , author=. 2021 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2021 , organization=

    2021

  72. [72]

    2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    Seil: simulation-augmented equivariant imitation learning , author=. 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2023 , organization=

    2023

  73. [73]

    International conference on machine learning , pages=

    Wilds: A benchmark of in-the-wild distribution shifts , author=. International conference on machine learning , pages=. 2021 , organization=

    2021

  74. [74]

    International Conference on Machine Learning , pages=

    The dormant neuron phenomenon in deep reinforcement learning , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  75. [75]

    arXiv preprint arXiv:2206.02126 , year=

    Learning dynamics and generalization in reinforcement learning , author=. arXiv preprint arXiv:2206.02126 , year=

    arXiv

  76. [76]

    arXiv preprint arXiv:2506.15544 , year=

    Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning , author=. arXiv preprint arXiv:2506.15544 , year=

    arXiv

  77. [77]

    Advances in Neural Information Processing Systems , volume=

    Adam on local time: Addressing nonstationarity in rl with relative adam timesteps , author=. Advances in Neural Information Processing Systems , volume=

  78. [78]

    arXiv preprint arXiv:2402.18762 , year=

    Disentangling the causes of plasticity loss in neural networks , author=. arXiv preprint arXiv:2402.18762 , year=

    arXiv

  79. [79]

    International Conference on Learning Representations , year=

    Kronecker-factored curvature approximations for recurrent neural networks , author=. International Conference on Learning Representations , year=

  80. [80]

    Advances in neural information processing systems , volume=

    Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation , author=. Advances in neural information processing systems , volume=

Showing first 80 references.