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Fine-tuning Behavioral Cloning Policies with Preference‑Based Reinforcement Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Behavioral CloningPreference-Based Reinforcement LearningReinforcement Learning
Abstract:

Deploying reinforcement learning (RL) in robotics, industry, and health care is blocked by two obstacles: the difficulty of specifying accurate rewards and the risk of unsafe, data-hungry exploration. We address this by proposing a two-stage framework that first learns a safe initial policy from a reward-free dataset of expert demonstrations, then fine-tunes it online using preference-based human feedback. We provide the first principled analysis of this offline-to-online approach and introduce BRIDGE, a unified algorithm that integrates both signals via an uncertainty-weighted objective. We derive regret bounds that shrink with the number of offline demonstrations, explicitly connecting the quantity of offline data to online sample efficiency. We validate BRIDGE in discrete and continuous control MuJoCo environments, showing it achieves lower regret than both standalone behavioral cloning and online preference-based RL. Our work establishes a theoretical foundation for designing more sample-efficient interactive agents.

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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 proposes a two-stage framework that first learns a safe initial policy from reward-free expert demonstrations, then fine-tunes it online using preference-based human feedback. It sits in the 'Unified Offline-Online Integration' leaf, which contains only three papers total, including this work. This represents a relatively sparse research direction within the broader taxonomy of 50 papers across 36 topics, suggesting the specific combination of principled offline-to-online integration with preference-based refinement remains underexplored compared to adjacent areas like pure preference learning or pure imitation learning.

The taxonomy reveals that neighboring research directions are substantially more populated. The parent branch 'Offline-to-Online Policy Refinement Frameworks' includes related leaves on reward model pre-training and personalization via action representations. Adjacent branches like 'Preference-Based Reward Learning and Policy Optimization' contain multiple subcategories with 2-3 papers each, focusing on direct preference optimization, reward modeling, and uncertainty-aware learning. The paper's position bridges these areas by combining demonstration-based initialization with preference-driven online refinement, distinguishing it from purely offline methods in 'Offline Preference Learning and Trajectory Generation' and fully online approaches in 'Interactive and Human-in-the-Loop Learning Systems'.

Among 20 candidates examined across three contributions, the analysis found limited prior work overlap. The first contribution (theoretical framework for offline-to-online preference learning) examined 9 candidates with none clearly refuting it. The second contribution (BRIDGE algorithm) examined only 1 candidate with no refutation. The third contribution (regret bound connecting offline data to online sample efficiency) examined 10 candidates, with 1 appearing to provide overlapping prior work. This suggests that within the limited search scope, the algorithmic and theoretical framework contributions appear relatively novel, though the regret bound analysis may have more substantial precedent in the examined literature.

Based on the top-20 semantic matches examined, the work appears to occupy a genuinely sparse intersection of offline imitation learning and online preference-based refinement. The limited number of sibling papers in its taxonomy leaf and the low refutation rate across contributions support this impression. However, the analysis explicitly covers only a narrow slice of potentially relevant literature, and the single refutable candidate for the regret bound suggests that deeper theoretical connections may exist in the broader offline-to-online RL literature not captured by this search.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: fine-tuning imitation learning policies with preference-based reinforcement learning. This field addresses how agents can move beyond simple behavioral cloning by incorporating human or oracle preferences to refine learned policies. The taxonomy reveals a rich landscape organized around several complementary themes. Offline-to-Online Policy Refinement Frameworks explore how to transition from static demonstration data to active policy improvement, often blending imitation with online exploration. Preference-Based Reward Learning and Policy Optimization focuses on extracting reward signals from comparative feedback, with methods ranging from classical inverse RL approaches like Scalable Inverse RL[5] and Bayesian Reward Inference[7] to modern direct optimization techniques such as Direct Preference Optimization[25]. Imitation Learning with Preference Signals and Large Language Model Alignment via Preference Learning capture the integration of preference data into both robotic control and language model fine-tuning, while Domain-Specific Applications and Interactive Human-in-the-Loop Learning Systems highlight practical deployments in robotics, autonomous vehicles, and interactive settings. Evaluation Frameworks and Benchmarking provide the infrastructure for measuring progress, and Scalable and Lifelong Learning Paradigms address long-term adaptation challenges. Several active lines of work reveal key trade-offs and open questions. One central tension is between offline methods that learn purely from logged data—such as Unlabeled Preference Data[19] and Offline Evaluation Budget[38]—and online or interactive approaches like Active RLHF[20] and Human-in-the-Loop Robotics[42], which can query for new preferences but incur higher annotation costs. Another contrast appears between model-based reward learning, where a reward function is explicitly estimated from preferences, and model-free direct policy optimization methods like Direct Preference Optimization[25] that bypass reward modeling. Behavioral Cloning Preference[0] sits within the Unified Offline-Online Integration branch, emphasizing seamless transitions from imitation to preference-driven refinement. It shares this branch with neighbors like Diffusion Human Preference[8] and TakeAD[47], which similarly aim to unify offline demonstration data with preference signals. Compared to purely offline methods or those requiring extensive online interaction, Behavioral Cloning Preference[0] occupies a middle ground, leveraging initial imitation policies while incorporating preference feedback to guide further refinement without fully committing to either extreme.

Claimed Contributions

First theoretical framework for offline-to-online preference learning

The authors establish the first principled theoretical framework for combining offline expert demonstrations with online preference-based reinforcement learning. They formalize this hybrid setting and derive regret bounds that explicitly connect the quantity of offline data to online sample efficiency.

9 retrieved papers
BRIDGE algorithm with uncertainty-weighted objective

The authors propose BRIDGE (Bounded Regret with Imitation Data and Guided Exploration), a novel algorithm that combines offline behavioral cloning with online preference-based learning through an uncertainty-weighted objective that constrains exploration to a confidence set constructed from offline data.

1 retrieved paper
Regret bound showing offline data reduces online regret

The authors prove that their algorithm achieves optimal square-root-T regret dependence while explicitly showing how the number of offline demonstrations n improves online performance. Their bound formally demonstrates that as offline data increases, online regret approaches zero.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

First theoretical framework for offline-to-online preference learning

The authors establish the first principled theoretical framework for combining offline expert demonstrations with online preference-based reinforcement learning. They formalize this hybrid setting and derive regret bounds that explicitly connect the quantity of offline data to online sample efficiency.

Contribution

BRIDGE algorithm with uncertainty-weighted objective

The authors propose BRIDGE (Bounded Regret with Imitation Data and Guided Exploration), a novel algorithm that combines offline behavioral cloning with online preference-based learning through an uncertainty-weighted objective that constrains exploration to a confidence set constructed from offline data.

Contribution

Regret bound showing offline data reduces online regret

The authors prove that their algorithm achieves optimal square-root-T regret dependence while explicitly showing how the number of offline demonstrations n improves online performance. Their bound formally demonstrates that as offline data increases, online regret approaches zero.

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