Linear interpolation between fine-tuned checkpoints has been shown to trace the Pareto front between competing objectives, but whether extrapolative weight averaging can extend such frontiers to new checkpoints useful at inference time, without additional RL training, remains unclear. We study this question in RL for competitive programming, where hidden unit tests under time and memory limits enforce both functional correctness and computational efficiency. Starting from a shared initialization, we train checkpoints under nested unit-test coverage: low-coverage rewards require passing smaller-input tests, while high-coverage rewards require passing progressively larger tests up to the full suite. This sweep reveals the emergence of a correctness-efficiency frontier: on hard problems, higher-coverage reward reduces optimization failures but increases correctness failures, leaving solve rate nearly unchanged. Interpolation between low- and high-coverage checkpoints recovers this frontier, while extrapolation extends it beyond the trained endpoints. Both the frontier and its extrapolative continuation appear across three inference settings, pure reasoning, tool use, and agentic coding, and across two model scales, 32B and 7B. At the problem level, moving along the frontier changes which problems are solved, making extrapolated checkpoints complementary policies in inference-time scaling. Ensembles with extrapolative weight averaging broaden coverage and improve pass@250 on LCB/hard by 3.3% over the best single checkpoint at matched sample budget. These results show that nested unit-test coverage in code RL induces a frontier that extrapolative weight averaging can navigate, extend, and exploit.

翻译：研究表明，微调检查点之间的线性插值能够追踪竞争目标之间的帕累托前沿，但外推权重平均是否能在无需额外强化学习训练的情况下，将此类前沿扩展到推理时可用的新检查点，目前仍不明确。我们在竞技编程的强化学习中研究了这一问题，其中隐藏单元测试在时间和内存限制下同时强制要求功能正确性和计算效率。从共享初始化开始，我们在嵌套单元测试覆盖下训练检查点：低覆盖奖励要求通过较小输入规模的测试，而高覆盖奖励则要求逐步通过更大规模的测试直至完整测试集。这一扫描揭示了正确性-效率前沿的出现：在困难问题上，高覆盖奖励减少了优化失败但增加了正确性失败，使得解决率几乎不变。低覆盖与高覆盖检查点之间的插值恢复了这一前沿，而外推则将其扩展到训练端点之外。这一前沿及其外推延续出现在三种推理设置（纯推理、工具使用和智能体编码）以及两种模型规模（32B和7B）中。在问题层面，沿前沿移动会改变被解决的问题，使得外推检查点成为推理时扩展中的互补策略。使用外推权重平均的集成方法扩大了覆盖范围，并在匹配样本预算下，将LCB/hard上的pass@250比最佳单一检查点提升了3.3%。这些结果表明，代码强化学习中的嵌套单元测试覆盖诱导出一个前沿，外推权重平均能够导航、扩展并利用这一前沿。