Jie Ren

Parallel test-time scaling for large language models normally generates every reasoning path to completion before internal confidence can score it, so compute on low-quality paths is already spent. PreG instead generates short prefixes in parallel, ranks them by internal confidence, and completes only the most promising candidates before aggregating as before. On Qwen3-8B and Llama-Nemotron-4B across math competition sets and GPQA-Diamond, it reaches the accuracy of the original method at a 1000k-token budget with fewer tokens, saving tokens in 54 of 60 configurations and up to 80%.

Parallel test-time scaling for large language models normally generates every reasoning path to completion before internal confidence can score it, so compute on low-quality paths is already spent. PreG instead generates short prefixes in parallel, ranks them by internal confidence, and completes only the most promising candidates before aggregating as before. On Qwen3-8B and Llama-Nemotron-4B across math competition sets and GPQA-Diamond, it reaches the accuracy of the original method at a 1000k-token budget with fewer tokens, saving tokens in 54 of 60 configurations and up to 80%.