# gpt-oss-120b gained 16.1pp task completion with 5% more tokens

> Source: <https://www.snipvote.com/story/cmszrozmw0004z4g1vzch7xm4>
> Published: 2026-08-19 08:10:53.049930+00:00

[Hugging Face](https://huggingface.co/blog/ibm-research/altk-evolve-hmm)

### gpt-oss-120b gained 16.1pp task completion with 5% more tokens

Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.

Tailoring agentic memory via selective retrieval yields a +16.1 percentage point task completion boost for mid-tier models like gpt-oss-120b at just +5% token overhead, whereas dumping the full memory set degrades their performance and inflates token costs by 50%. This means you cannot use a one-size-fits-all context injection strategy in production; you must restrict smaller models to a tight, retrieved subset of distilled guidelines to prevent cognitive drowning while reserving massive, full-set memory injection only for frontier models like DeepSeek-V3.2.

16.1pp task-completion gain on gpt-oss-120b with only +5% token cost when using selective memory retrieval instead of full guideline injection. This means you can ship stronger agents on mid-tier models without blowing up inference budgets—just swap static prompts for a lightweight retrieval layer that serves only the most relevant past lessons per task.