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Rethinking How AI Thinks: "Markovian Thinking" Promises Efficient Reasoning

Montreal, Canada - Researchers have introduced a novel paradigm for training large language models (LLMs) that could dramatically improve their reasoning capabilities while significantly reducing computational costs. Dubbed “Markovian Thinking,” this approach reimagines the “thinking environment” for LLMs, allowing them to generate longer and more complex chains of thought without the quadratic increase in computational demands that plagues current methods.

The current standard for training LLMs to perform complex reasoning, known as “long chain-of-thought” (LongCoT), involves feeding the model the entire history of its thought process as context. While this allows for intricate reasoning, it leads to an ever-expanding context window. For attention-based models, this translates to a quadratic increase in computation and memory requirements as the reasoning length grows. This effectively creates a bottleneck, limiting the depth of reasoning LLMs can achieve within practical computational constraints.

The “Markovian Thinking” paradigm, as detailed in a recent paper, offers a solution by decoupling the length of the thought process from the size of the context the model needs to process at any given moment. Instead of maintaining a continuously growing history, this new approach divides the reasoning process into fixed-size “chunks.”

Imagine an LLM tackling a complex math problem. In the traditional LongCoT approach, it would meticulously record every intermediate step, every calculation, and every hypothesis in a growing log. If the problem requires hundreds of steps, this log becomes enormous, requiring substantial computing power to process.

With Markovian Thinking, however, the LLM works in stages. It might, for instance, work through a set of related calculations within a “chunk” of manageable size. At the end of that chunk, the environment “resets” the full context. Crucially, instead of forgetting everything, the model is prompted to summarize its progress in a concise “textual state” – essentially a brief memo of the key findings from that chunk. This summary then becomes the starting point for the next chunk.

The research team implemented this concept with an environment called “Delethink.” They demonstrated that LLMs trained with Delethink can achieve reasoning lengths of up to 24,000 tokens, matching or even surpassing LongCoT models trained with the same computational budget. Even more impressively, Delethink models continue to improve their performance at test time, long after LongCoT models plateau.

This efficiency gain is substantial. The researchers estimate that achieving an average thinking length of 96,000 tokens with LongCoT-RL could cost approximately 27 H100 GPU-months, while Delethink could achieve the same with just 7 H100 GPU-months.

Furthermore, the study reveals that off-the-shelf reasoning models, even without specific training in this new paradigm, often exhibit “Markovian traces” when presented with challenges designed for Delethink. This suggests that the underlying architecture of many current LLMs is already predisposed to this style of chunked reasoning, making Delethink a practical and scalable solution.

By breaking down complex reasoning into manageable, Markovian steps, this research opens a path towards LLMs that can tackle vastly more intricate problems with significantly reduced computational overhead. This could pave the way for more powerful and efficient AI systems across a wide range of applications, from scientific discovery to complex problem-solving.