August 24, 2025

Artificial Intelligence - Think in Blocks Adaptive Reasoning from Direct Response to Deep Reasoning

4 minutes

Hey PaperLedge listeners, Ernis here, ready to dive into some seriously cool research! Today, we're tackling a paper about making those brainy AI systems – you know, the Large Language Models or LLMs – even smarter and, get this, more efficient.

Think of LLMs like a super-smart student trying to solve a tough math problem. They use "chains of thought," which are basically step-by-step explanations to arrive at the answer. The longer the chain, the more thorough the reasoning... usually. But sometimes, that student overthinks it! They write pages and pages when a simple calculation would have done the trick. It's a waste of time and effort, right?

Well, that's the problem this paper addresses. Can we teach LLMs to be like that efficient student who knows exactly how much effort to put into each problem?

The researchers introduce something called "Think in Blocks." Imagine breaking down a complex task into manageable chunks, like building with LEGOs. Each LEGO block represents a step in the reasoning process. The brilliant part? The LLM gets to decide how many blocks it needs before even starting!

Here's how they did it:

First, they created a system where the LLM explicitly predicts how many "reasoning blocks" it will use. It's like the LLM saying, "Okay, this looks like a 3-block problem."

Next, they trained the LLM to be a good judge of difficulty. Think of it like teaching the LLM to recognize whether it's solving a simple arithmetic problem or something requiring advanced calculus. This training involves Supervised Fine-Tuning, reward-guided Direct Preference Optimization, and Reinforcement Learning. Sounds complicated, but it's all about rewarding the LLM for making smart choices about how deeply to reason.

Finally, they gave the LLM the power to change its mind! During the actual task, the LLM can adjust the number of blocks it uses on the fly. It's like realizing halfway through that the problem is easier (or harder) than you thought and adjusting your approach accordingly.

So, why does this matter? Well, for a few reasons:

For AI developers: This is huge! It means we can build more efficient LLMs that use less computational power. That translates to lower costs and faster response times.

For businesses: Imagine customer service chatbots that can quickly and accurately answer questions without getting bogged down in unnecessary details. Think faster resolutions and happier customers!

For everyone: Ultimately, this research is about making AI more adaptable and intelligent. It's about creating systems that can learn and reason more like humans, which could lead to breakthroughs in all sorts of fields, from medicine to education.

"Think in Blocks enables adaptive reasoning – from zero to deep reasoning – by partitioning the reasoning process into a tunable number of blocks."

This quote really highlights the core of the research: giving LLMs the ability to think flexibly and efficiently.

Here are a couple of things that came to mind while reading this paper that we could discuss:

How might this "Think in Blocks" approach impact the creativity of LLMs? Could limiting the reasoning depth stifle innovative solutions, or does it actually force the AI to be more resourceful?

Could this framework be adapted to other types of AI, beyond just Large Language Models? What other areas of AI research could benefit from this kind of adaptive reasoning?

That's all for today's deep dive! I hope you found this paper as fascinating as I did. Until next time, keep those gears turning!

Credit to Paper authors: Yekun Zhu, Guang Chen, Chengjun Mao

...more

View all episodes

By ernestasposkus