代码RL领域首次系统揭示正确性与效率的权衡前沿，做编程竞赛或代码生成优化的团队可以直接用外推权重平均提升模型性能，无需额外训练成本。
该研究探讨了在代码强化学习（RL）中，通过外推权重平均（extrapolative weight averaging）能否在不额外训练的情况下，扩展微调检查点之间的帕累托前沿。研究者针对竞争性编程任务，使用嵌套单元测试覆盖（从低覆盖到高覆盖）训练检查点，发现正确性与效率之间存在权衡：高覆盖奖励减少优化失败但增加正确性失败，整体解决率不变。通过低覆盖和高覆盖检查点的插值可恢复该前沿，而外推则能超越训练端点。该现象在纯推理、工具使用和智能体编码三种推理设置以及32B和7B两种模型规模下均成立。外推权重平均的集成方法在LCB/hard基准上，以相同样本预算将pass@250提升了3.3%。结果表明，嵌套单元测试覆盖诱导的前沿可通过外推权重平均进行导航、扩展和利用。
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
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.