In-Context Learning as Rate–Distortion Optimization

In-context learning (ICL) is a practical way to adapt large models, yet under strict context limits, it remains unclear how to spend scarce tokens without being misled by noisy, redundant, or conflicting demonstrations. We address this gap by targeting token-budgeted context construction: how to select and compress demonstrations so the prompt carries maximal task-relevant signal with minimal predictive distortion. We propose RDCO, a deterministic, training-free optimizer that scores demonstrations by marginal task information per token, penalizes redundancy and prefix-conditioned conflicts, and finally compacts the selected context under a bounded predictive-divergence constraint to control drift. Across a 10-dataset ICL suite spanning classification and structured generation, RDCO achieves the best average performance (63.26 Acc. on classification and 60.26 EM on generation), improving over the strongest classification and generation baselines by 2.20 and 2.26 points, respectively, and improving the 10-task overall average by 4.94 points under the same budget. Our results suggest that viewing prompts as finite-capacity messages yields a principled and effective path to more reliable and token-efficient ICL.