OpenNovelty.org

Optimistic Task Inference for Behavior Foundation Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Behavior Foundation ModelsZero-Shot Reinforcement LearningDeep Reinforcement LearningFast Adaptation
Abstract:

Behavior Foundation Models (BFMs) are capable of retrieving high-performing policy for any reward function specified directly at test-time, commonly referred to as zero-shot reinforcement learning (RL). While this is a very efficient process in terms of compute, it can be less so in terms of data: as a standard assumption, BFMs require computing rewards over a non-negligible inference dataset, assuming either access to a functional form of rewards, or significant labeling efforts. To alleviate these limitations, we tackle the problem of task inference purely through interaction with the environment at test-time. We propose OpTI-BFM, an optimistic decision criterion that directly models uncertainty over reward functions and guides BFMs in data collection for task inference. Formally, we provide a regret bound for well- trained BFMs through a direct connection to upper-confidence algorithms for linear bandits. Empirically, we evaluate OpTI-BFM on established zero-shot benchmarks, and observe that it enables successor-features-based BFMs to identify and optimize an unseen reward function in a handful of episodes with minimal compute overhead.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces OpTI-BFM, an optimistic decision criterion enabling behavior foundation models to infer task objectives through minimal online interaction rather than requiring labeled datasets or explicit reward functions. Within the taxonomy, it occupies the 'Optimistic and Uncertainty-Driven Task Inference' leaf under 'Task Inference and Reward Specification Methods'. Notably, this leaf contains only the original paper itself—no sibling papers—indicating a relatively sparse research direction within the broader field of 31 surveyed papers across multiple branches.

The taxonomy reveals neighboring approaches in sibling leaves: 'Preference-Based and Human Feedback Methods' (1 paper) and 'Imitation Learning and Behavioral Cloning' (2 papers). These alternatives address task specification through human preferences or expert demonstrations rather than autonomous exploration. The scope notes clarify that OpTI-BFM's online uncertainty-driven approach explicitly excludes offline demonstration methods and extensive labeling efforts, positioning it as a distinct paradigm. Related work on 'Zero-Shot and Fast Adaptation Mechanisms' (1 paper) shares the goal of rapid task adaptation but differs in requiring pre-learned embeddings rather than online interaction.

Among 30 candidates examined through semantic search, none provided clear refutation for any of the three core contributions: the OpTI-BFM algorithm (10 candidates examined), the regret bound via linear bandit connection (10 candidates), and the online task inference framework (10 candidates). This limited search scope suggests the specific combination of optimistic exploration, successor features, and formal regret guarantees for BFMs may represent a novel synthesis. However, the absence of refutable prior work reflects the search scale rather than exhaustive coverage of related bandit or meta-learning literature.

Given the sparse taxonomy leaf and limited search scope, the work appears to occupy a relatively unexplored niche within behavior foundation models. The formal connection to upper-confidence bandit algorithms and the focus on data-efficient task inference through interaction distinguish it from neighboring preference-based or imitation-based methods. However, the analysis covers top-30 semantic matches and does not capture potential overlaps in broader reinforcement learning or active learning communities outside the foundation model framing.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: task inference for behavior foundation models through online interaction. The field addresses how agents can discover or refine their objectives by engaging with environments and users in real time, rather than relying solely on pre-specified reward functions. The taxonomy organizes this landscape into several main branches. Task Inference and Reward Specification Methods explore techniques for eliciting goals from interaction data, including optimistic exploration strategies and uncertainty-driven approaches. Foundation Model Training Paradigms and Architectures examine how large-scale models are built and adapted, spanning self-play mechanisms, imitation from demonstrations, and fine-tuning protocols. Domain-Specific Foundation Model Applications investigate deployments in areas such as robotics, web agents, and mobility systems, while Theoretical Frameworks and Conceptual Foundations provide the mathematical and conceptual underpinnings for decision-making and learning guarantees. User Behavior Modeling and Personalization Systems focus on inferring individual preferences and characteristics to tailor agent behavior accordingly. A particularly active line of work centers on methods that balance exploration with task discovery: Optimistic Task Inference[0] exemplifies uncertainty-driven strategies that guide agents toward informative interactions, contrasting with more passive imitation approaches like Fast Imitation[2] or behavioral cloning schemes such as Fine-tuning Behavioral Cloning[23]. Meanwhile, Interactive Agent Foundation[1] and Interactive Agent Meta-Learning[21] emphasize meta-learning and rapid adaptation through online feedback, highlighting trade-offs between sample efficiency and generalization across diverse tasks. Another strand, represented by Foundation Models Decision Making[3] and Foundation Model Self-Play[12], investigates how large pre-trained models can bootstrap their own training signals or engage in self-improvement loops. Optimistic Task Inference[0] sits naturally within the uncertainty-driven cluster, sharing conceptual ground with Active Inference HCI[4] in its emphasis on proactive information gathering, yet differing in its focus on optimistic bounds rather than purely Bayesian inference. This positioning underscores ongoing questions about how best to integrate exploration incentives with foundation model architectures.

Claimed Contributions

OpTI-BFM: Optimistic Task Inference for Behavior Foundation Models

The authors introduce OpTI-BFM, a method that enables task inference through active interaction with the environment at test-time rather than requiring labeled offline datasets. It uses optimistic decision-making to guide data collection by modeling uncertainty over reward functions through confidence ellipsoids.

10 retrieved papers
Regret bound for well-trained BFMs via linear bandit connection

The authors establish theoretical guarantees by connecting the task inference problem to linear contextual bandits, proving that OpTI-BFM achieves sublinear regret when the underlying BFM is well-trained and certain assumptions hold.

10 retrieved papers
Online task inference framework for BFMs

The authors propose a new framework where task inference occurs online during deployment by actively collecting data, removing the need for pre-training dataset access and reducing labeling requirements compared to standard offline inference pipelines.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

OpTI-BFM: Optimistic Task Inference for Behavior Foundation Models

The authors introduce OpTI-BFM, a method that enables task inference through active interaction with the environment at test-time rather than requiring labeled offline datasets. It uses optimistic decision-making to guide data collection by modeling uncertainty over reward functions through confidence ellipsoids.

Contribution

Regret bound for well-trained BFMs via linear bandit connection

The authors establish theoretical guarantees by connecting the task inference problem to linear contextual bandits, proving that OpTI-BFM achieves sublinear regret when the underlying BFM is well-trained and certain assumptions hold.

Contribution

Online task inference framework for BFMs

The authors propose a new framework where task inference occurs online during deployment by actively collecting data, removing the need for pre-training dataset access and reducing labeling requirements compared to standard offline inference pipelines.

Table of Contents