Training large language models on massive datasets is computationally expensive, yet empirical evidence suggests that substantial portions of training examples contribute minimally to final performance. Data subset selection addresses this inefficiency by identifying small, high-utility subsets under resource constraints. However, example utility is inherently multi-faceted, encompassing uncertainty, distributional rarity, and diversity signals that are heterogeneous and typically combined through ad hoc weighted sums lacking theoretical grounding. We propose a market-based framework that treats each training example as a tradeable contract and employs the Logarithmic Market Scoring Rule to aggregate multiple utility signals into coherent prices. Heterogeneous signals act as traders, a single liquidity parameter controls concentration versus smoothing, and topic-wise normalization ensures calibrated aggregation. Token budgets are handled explicitly through a price-per-token decision rule with an interpretable length-bias parameter. We establish theoretical connections to maximum-entropy aggregation and provide utility recovery guarantees under noisy but monotone signals. On GSM8K mathematical reasoning under strict 60k-token budgets, our selector achieves parity with strong single-signal baselines while exhibiting lower variance and incurring less than 0.1 GPU-hour overhead. On AGNews classification at 5-25\% retention rates, the market formulation delivers competitive accuracy with improved stability. Our framework unifies multi-signal data curation under fixed computational budgets for prompt-level reasoning and classification tasks.

翻译：在大规模数据集上训练大型语言模型的计算成本高昂，然而经验证据表明，大量训练样本对最终性能的贡献微乎其微。数据子集选择通过识别资源约束下的小规模、高效用子集来解决这一效率问题。然而，样本效用本质上是多方面的，包含不确定性、分布稀有性和多样性信号，这些信号具有异质性，通常通过缺乏理论依据的临时加权求和进行组合。我们提出一个基于市场的框架，将每个训练样本视为可交易合约，并采用对数市场评分规则将多个效用信号聚合成一致的价格。异质信号充当交易者，单一流动性参数控制集中度与平滑度，主题归一化确保校准聚合。通过具有可解释长度偏差参数的每令牌价格决策规则，显式处理令牌预算。我们建立了与最大熵聚合的理论联系，并在噪声但单调信号下提供了效用恢复保证。在严格的60k令牌预算下，于GSM8K数学推理任务中，我们的选择器实现了与强单信号基线的性能持平，同时表现出更低的方差和小于0.1 GPU小时的开销。在AGNews分类任务中，以5-25%的保留率，市场公式在保持竞争力的准确率的同时提供了更高的稳定性。我们的框架在固定计算预算下，为提示级推理和分类任务统一了多信号数据筛选。