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MLPerf Training adds an LLM post-training benchmark, starting with round v6.1
MLCommons announced on September 24, 2026 that MLPerf Training is adding an LLM post-training benchmark, beginning with the v6.1 submission round in October 2026. The benchmark reports the wall-clock time a system needs to reach a quality target when post-training Qwen3.5-397B-A17B with reinforcement learning with verifiable rewards, using Group Relative Policy Optimization, on Python software engineering problems from the R2E-Gym dataset. Agents work in sandboxed Linux containers, and the specification and reference implementation are in the mlcommons/training repository.1
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USASI. (2026, October 13). MLPerf Training adds an LLM post-training benchmark, starting with round v6.1. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/news/mlperf-training-adds-llm-post-training-benchmark/
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@misc{usasi_news_mlperf_training_adds_llm_post_training_benchmark,
author = {{USASI}},
title = {MLPerf Training adds an LLM post-training benchmark, starting with round v6.1},
year = {2026},
month = oct,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/news/mlperf-training-adds-llm-post-training-benchmark/}},
note = {United States of America Superintelligence. Last updated 2026-10-13}
}The date is when this page was last updated. Add the date you read it if your style needs one.