Scaling Test-Time Compute: Qwen-3.6-27B and Gemma-4-31B Outperform Claude Mythos in Code Optimization

A new implementation leveraging an advanced compute scaffold has demonstrated that scaling test-time compute for Qwen-3.6-27B and Gemma-4-31B allows these models to surpass Claude Mythos in specific benchmarks related to code optimization and execution speedups.

Scaling Inference via Advanced Scaffolding

Recent experiments conducted by researcher u/Ryoiki-Tokuiten demonstrate a significant performance leap in code optimization tasks by shifting the computational burden from training to the inference phase. By implementing a sophisticated compute scaffold, the researcher scaled test-time compute by approximately 25x to 40x relative to the original baseline models.

The "Max Mode" Architecture

To achieve these results, the system was configured in a "max mode" setting, utilizing a multi-layered approach to problem-solving and verification. The architectural configuration includes the following parameters:

• Exploration Breadth: The system explores up to 5 concurrent branches to evaluate multiple solution paths.
• Iterative Correction: A loop depth of 10 iterations is employed to refine the output through continuous self-correction.
• Selective Hypothesis Testing: The scaffold utilizes 6 branch-aware selective hypotheses. These hypotheses are revised every two iterations to test various claims, local speedups, or entirely different algorithmic designs independently.

Mechanism of Hypothesis Injection

The core of this approach lies in the selective injection of these hypotheses into specific branch contexts. This allows the model to independently test divergent algorithmic designs and local optimizations, iteratively pruning ineffective paths and refining the most promising candidates to achieve superior code speedups.

Note: The provided source material is a brief excerpt; specific benchmark metrics and the full codebase of the scaffold were not included in the original description.