VQV Signal
According to a post on X by @ilblackdragon, @0xrosh improved the Qwen3.5-0.8B model by 19% on the contest's private eval. The post says the gain apparently came from dampening the embeddings of the "Wait" and "Hmm" tokens. The snippet presents this as the first big result of the Delta League contest. The post itself is the only source for these details, and the snippet does not describe the method in further depth.
According to a post on X by @ilblackdragon, @0xrosh improved the Qwen3.5-0.8B model by 19% on the contest's private eval. The post says the gain apparently came from dampening the embeddings of the "Wait" and "Hmm" tokens. The snippet presents this as the first big result of the Delta League contest. The post itself is the only source for these details, and the snippet does not describe the method in further depth.
AI-assisted summary based on the listed source.
@ilblackdragon says @0xrosh improved Qwen3.5-0.8B by 19% on the contest's private eval, apparently by dampening the embeddings of "Wait" and "Hmm" tokens. Why it matters: Direct weight edits that tame overthinking show small models still have cheap, unexplored gains. Original post on X
Builders working with small language models may see this as a sign that direct weight edits to reduce overthinking can produce measurable gains. Watch for further details from the Delta League contest and whether the method holds up when others test it.
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