Reddit Research Notes: A 7-Layer Duplication Trick Climbs the Open LLM Leaderboard

A post in r/MachineLearning is drawing attention to a blog write-up that claims a surprisingly cheap route to measurable LLM gains. The author says he took Qwen2-72B, duplicated a specific block of seven middle layers without changing any weights, and produced a model that topped the Hugging Face Open LLM Leaderboard in 2024. The striking part is not just the result, but the constraint: no gradient updates, no weight merging, and no giant cluster.

The argument in the linked essay is that transformer stacks may contain functional circuits that only work when preserved as a block. Single-layer duplication reportedly does nothing. Duplicating too many layers gets worse. But copying a circuit-sized middle segment created better benchmark behavior, which led the author to frame the discovery as a kind of LLM neuroanatomy. The work was developed on consumer hardware, starting with 2x RTX 4090 GPUs, which is part of why the post resonated so strongly.

The Reddit comments treated the claim as weird but plausible. Some readers connected it to older work showing that transformer layers can sometimes be removed or shuffled with less damage than expected, suggesting the residual stream may be more stable than a rigid pipeline view implies. Others speculated about looped circuits, halting behavior, or new forms of lightweight architectural scaling that do not require full retraining.

There are obvious reasons to stay cautious. A leaderboard result is not the same thing as a general capability breakthrough, and the hypothesis still needs broader replication across model families and tasks. But the broader signal is important: low-budget researchers are still able to surface nontrivial architectural behavior, especially when they study models as systems instead of treating them only as weights to fine-tune.

If nothing else, the post is a reminder that open-weight LLM progress is not only a compute race. Sometimes the interesting result comes from asking what parts of the stack are actually doing the work. Original source: technical blog post. Community discussion: r/MachineLearning.