Learn to build a custom harness, folks.
This Microsoft work builds a harness to improve long-context reasoning without synthetic data or post-training.
(bookmark it)
They show that Llama3-8B gains 64.18 points on average across long-context benchmarks with no training at all. The gain comes from the harness.
It breaks long-context tasks into capability primitives. A strong agent then rewrites those primitives, the state they track, and the control logic over many rounds of self-improvement on a development set.
The harness built for Llama3-8B transfers to Qwen2.5-3B. On HELMET and RULER, it beats NExtLong and PolicyLong, which rely on data synthesis and training, at less than half their estimated cost.
The harness is written by GPT-5.6-Sol, and results drop sharply when the small models build their own.
The takeaway: before post-training a small model for long context, try improving the harness around it.
https://arxiv.org/abs/2611.10463
Chat with Paper: https://academy.dair.ai/papers/longcapharness-capability-guided-auto-harnessing-for-long-context-reasoning-2611.10463