Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop Transformers (PLT) alleviate this cost through cross-loop position offsets (CLP) and shared-KV gated sliding-window attention, making loop count a practical design choice. We therefore study PLT loop-count selection through a gain--cost view: an extra loop may refine representations, but CLP also introduces a positional mismatch at each loop boundary. We instantiate this study by training LoopCoder-v2, a family of 7B PLT coders with different loop counts, from scratch on 18T tokens, followed by matched instruction tuning and evaluation. Empirically, the two-loop variant delivers broad gains over the non-looped baseline across code generation, code reasoning, agentic software engineering, and tool-use benchmarks, improving SWE-bench Verified from 43.0 to 64.4 points and Multi-SWE from 14.0 to 31.0 points. In contrast, variants with three or more loops regress, revealing a strongly non-monotonic loop-count effect. Our diagnostics show that loop 2 provides the main productive refinement, while later loops yield diminishing, oscillatory updates and reduced representational diversity. Because the CLP-induced mismatch remains roughly fixed as refinement gains shrink, the offset cost increasingly dominates. This gain--cost trade-off explains PLT's saturation at two loops and provides diagnostics for loop-count selection.

翻译：循环式Transformer通过重复应用共享模块以扩展潜在计算，但顺序循环会随着循环次数增加而提高延迟和KV缓存内存。并行循环Transformer通过跨循环位置偏移（CLP）和共享KV门控滑动窗口注意力缓解了这一代价，使得循环次数成为实用的设计选择。因此，我们从收益-成本视角研究PLT循环次数的选择：额外循环可能优化表征，但CLP也在每个循环边界引入位置失配。我们通过训练LoopCoder-v2（一组具有不同循环次数的7B参数PLT编码器系列）从头在18T令牌上训练，随后进行匹配的指令微调和评估来实例化此项研究。实验表明，两循环变体在代码生成、代码推理、智能体软件工程和工具使用基准上相比非循环基线取得了广泛改进，将SWE-bench Verified从43.0分提升至64.4分，Multi-SWE从14.0分提升至31.0分。相反，具有三个或更多循环的变体性能下降，揭示了强烈的非单调循环次数效应。我们的诊断表明，循环2提供了主要的生产性优化，而后续循环产生递减的振荡更新和降低的表征多样性。由于CLP诱导的失配在优化收益缩小时保持大致固定，偏移成本逐渐占主导。这种收益-成本权衡解释了PLT在两循环时达到饱和，并为循环次数选择提供了诊断依据。