LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling

Hugging Face Daily Papers Papers
code-generation transformers loop-architecture efficiency test-time-scaling parallel-loop

Summary

LoopCoder-v2 proposes Parallel Loop Transformers (PLT) for efficient test-time computation scaling in code generation, showing that two loops yield significant gains while more loops cause diminishing returns and positional mismatch costs.

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.
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Abstract

Parallel loop Transformers achieve better code generation performance with two loops due to refined representations, while additional loops cause diminishing returns and increased positional mismatch costs.

Looped Transformersscale 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 throughcross-loop position offsets(CLP) andshared-KV gated sliding-window attention, making loop count a practical design choice. We therefore study PLTloop-count selectionthrough 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 trainingLoopCoder-v2, a family of 7B PLT coders with different loop counts, from scratch on 18T tokens, followed by matchedinstruction tuningand 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, improvingSWE-benchVerified from 43.0 to 64.4 points andMulti-SWEfrom 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 forloop-count selection.

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