LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Authors: name: Jian Yang, name: Shawn Guo, name: Wei Zhang, name: Tianyu Zheng, name: Yaxin Du, name: Haau-Sing Li, name: Jiajun Wu, name: Yue Song, name: Yan Xing, name: Qingsong Cai, name: Zelong Huang, name: Chuan Hao, name: Ran Tao, name: Xianglong Liu, name: Wayne Xin Zhao, name: Mingjie Tang, name: Weifeng Lv, name: Ming Zhou, name: Bryan Dai

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.