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Teaching AI to Think Out Loud Without the Rambling

Santthosh Selvadurai

09 Mar 2026 — 1 min read

Teaching AI to Think Less and Say More

TL;DR — Researchers found that AI reasoning models ramble too much, and simply asking them to "be concise" then training them to do it naturally cuts their thinking by half while making them more accurate.

What It Is

When you ask modern AI models like GPT-4 or Claude to solve a math problem, they generate thousands of words of internal reasoning before answering. Even for "2+2," they might write 500 tokens exploring whether you meant binary. This new method, called OPSDC, teaches models to be concise using a clever trick: they prompt the same model to "solve concisely," capture how it behaves differently, then train the base model to act that way without needing the prompt. No fancy reward systems, no human-labeled "correct" reasoning traces—just the model learning from its own more efficient behavior. The result? On math benchmarks, they cut reasoning length by 35-59% while accuracy jumped by 9-16 percentage points.

Why It Matters

Inference costs drop immediately — If your LLM uses half the tokens to reach better answers, you've just cut your API bills and latency in half without switching models or compromising quality.
It's difficulty-aware automatically — The method compresses easy problems aggressively (saving you money on simple queries) while preserving long reasoning chains for genuinely hard problems, without you manually tuning anything.
No labeled data required — Unlike other compression techniques that need ground-truth answers or curated reasoning examples, this works by distillation alone, making it practical for domains where you don't have perfect training data.

One Thing to Try

If you're working with reasoning models today, add a simple "solve this concisely, avoid unnecessary steps" instruction to your prompts and A/B test it against your baseline. The paper shows the models already know how to compress—they just need permission. You'll likely see immediate cost savings on straightforward queries while maintaining quality on complex ones.

Link to paper

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