Adaptive Batch-Wise Sample Scheduling for Direct Preference Optimization
Introduces SamS, an adaptive sample scheduling algorithm that improves large language model preference alignment by over 12% using model state feedback.
Key Findings
Methodology
This paper proposes SamS, framing sample scheduling as a contextual bandit problem. It encodes each sample with internal model states, designs a reward combining batch loss reduction and uncertainty, and employs an exploration-exploitation strategy with lagged updates and auxiliary networks. The approach integrates seamlessly into existing DPO workflows, significantly boosting performance while maintaining low computational overhead.
Key Results
- In benchmarks like AlpacaEval 2 and MT-Bench, DPO+SamS improves win rates by 3.0%-12.4%, demonstrating robustness against noisy data, with minimal additional training costs. The method outperforms baselines across multiple metrics, with performance gains of up to 12%.
- Across diverse datasets, integrating SamS with preference optimization methods yields an average accuracy increase of 2.7%, validating its effectiveness in sample prioritization and noise mitigation.
- Ablation studies confirm that the reward components—batch loss change and uncertainty—are critical for performance, enabling the model to focus on high-quality, informative samples.
Significance
This work addresses a core bottleneck in large language model alignment—efficiently utilizing limited, noisy preference data. By dynamically scheduling samples based on model state, it enhances robustness, data efficiency, and generalization. The approach offers a practical, plug-and-play solution that advances the state-of-the-art in preference optimization, with broad implications for AI safety, personalization, and scalable alignment.
Technical Contribution
The paper introduces a novel formulation of sample scheduling as a contextual bandit problem, with a reward mechanism that captures training progress and sample uncertainty. The design of a lightweight, exploration-aware scheduler with lagged updates and auxiliary networks distinguishes it from prior static sampling methods, providing theoretical and empirical improvements in model alignment performance.
Novelty
This is the first systematic framing of preference sample scheduling within a contextual bandit framework, leveraging internal model states for dynamic sample prioritization. The combination of reward design, lagged updates, and exploration strategies constitutes a significant innovation over existing static or heuristic sampling techniques, leading to substantial performance gains.
Limitations
- The effectiveness depends on accurate internal state encoding; poor representations may reduce scheduling quality.
- In scenarios with extremely noisy or inconsistent preference labels, the scheduler's guidance may degrade.
- Additional computational overhead, though minimal, may pose challenges for very large-scale training or real-time applications.
Future Work
Future research will explore multi-modal and multi-task extensions, adaptive reward functions, and more sophisticated state representations. Combining this approach with reinforcement learning or meta-learning could further improve robustness and sample efficiency, pushing towards fully autonomous, scalable preference alignment systems.
AI Executive Summary
Preference alignment is crucial for deploying large language models safely and effectively, but current methods face challenges in data quality, efficiency, and robustness. Traditional approaches like RLHF involve complex reward modeling and iterative training, which are costly and sensitive to noisy labels. Direct Preference Optimization (DPO) simplifies this process but still relies on static sample selection, limiting its efficiency.
This paper introduces SamS, an innovative sample scheduling algorithm that dynamically selects training samples based on the internal states of the model. By framing the scheduling problem as a contextual bandit, SamS employs a reward function that combines the reduction in batch loss and the model's uncertainty about samples. The scheduler uses a lightweight encoder and two neural networks—one for exploitation, one for exploration—to estimate sample quality. It updates its parameters with a lagged strategy, avoiding additional computational costs.
Extensive experiments on benchmarks like AlpacaEval 2 and MT-Bench demonstrate that integrating SamS with DPO yields performance improvements of up to 12% in win rates, with enhanced robustness against noisy data. The method also generalizes well across different preference datasets and optimization algorithms, confirming its broad applicability. Importantly, the approach maintains low overhead, making it practical for large-scale deployment.
Overall, SamS offers a promising new direction for improving large language model alignment by intelligently prioritizing training samples. It addresses key limitations of existing methods, providing a scalable, robust, and efficient solution. Future work will focus on multi-modal extensions, adaptive reward mechanisms, and further reducing computational costs, aiming to realize fully autonomous, high-quality model alignment in diverse real-world scenarios.
Deep Dive
Abstract
Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its performance is highly dependent on the quality of the underlying human preference data. To address this bottleneck, prior work has explored various data selection strategies, but these methods often overlook the impact of the evolving states of the language model during the optimization process. In this paper, we introduce a novel problem: Sample Scheduling for DPO, which aims to dynamically and adaptively schedule training samples based on the model's evolving batch-wise states throughout preference optimization. To solve this problem, we propose SamS, an efficient and effective algorithm that adaptively selects samples in each training batch based on the LLM's learning feedback to maximize the potential generalization performance. Notably, without modifying the core DPO algorithm, simply integrating SamS significantly improves performance across tasks, with minimal additional computational overhead. This work points to a promising new direction for improving LLM alignment through batch-wise sample selection, with potential generalization to RLHF and broader supervised learning paradigms.