Model Predictive Task Sampling for Efficient and Robust Adaptation
Bibliographic record
Abstract
<title>Abstract</title> Foundation models have revolutionized general-purpose problem-solving, offering rapidtask adaptation through pretraining, meta-training, and finetuning. Recent crucial advances in these paradigms reveal the importance of challenging taskprioritized sampling to enhance adaptation robustness under distribution shifts. However, ranking task difficulties over iteration as a preliminary step typically requiresexhaustive task evaluation, which is practically unaffordable in computation and data-annotation. This study provides a novel perspective to illuminate the possibility of leveraging thedual importance of adaptation robustness and learning efficiency, particularly inscenarios where task evaluation is risky or costly, such as iterative agent-environmentinteractions for robotic policy evaluation or computationally intensive inference steps forfinetuning foundation models. Firstly, we introduce Model Predictive Task Sampling (MPTS), a framework that bridgesthe task space and adaptation risk landscape, providing a theoretical foundation forrobust active task sampling. MPTS employs a generative model to characterize the episodic optimization process andpredicts task-specific adaptation risk via posterior inference. The resulting risk learner amortizes the costly evaluation of task adaptationperformance and provably approximates task difficulty rankings. MPTS seamlesslyintegrates into zero-shot, few-shot, and supervised finetuning settings. Empirically, we conduct extensive experiments in pattern recognition using foundationmodels and sequential decision-making. Our results demonstrate that MPTS significantly enhances adaptation robustness for tailor out-of-distribution (OOD) tasks and improves learning efficiency compared to state-of-the-art (SOTA) methods. The code is available at the project site https://github.com/thu-rllab/MPTS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".