In the research of artificial intelligence, reinforcement learning has long been proven as a very powerful learning method in multiple fields. Especially in the field of Go, the AlphaGo system developed by DeepMind is a famous example. This system not only successfully defeated Lee Sedol, one of the top players in the world of Go, but also demonstrated the potential of reinforcement learning in solving complex problems.
The Initial Exploration of AlphaGo and Reinforcement Learning
AlphaGo's training approach differs from traditional supervised learning models. In supervised learning, the model learns Go techniques by imitating a large number of games played by expert players. Although this method can help improve the model’s performance, its ultimate ability remains limited by human capabilities. Even the best players struggle to break through this bottleneck.
In reinforcement learning, the model does not simply mimic human players but instead improves by playing against itself, repeatedly trying different moves and using statistical analysis to find the optimal strategies for winning. This learning method is not constrained by human cognition, allowing it to discover strategies that traditional players might overlook, even surpassing the level of top players.
Move 37: A Brilliant Move Unimaginable to Humans
The reinforcement learning process of AlphaGo has many surprising discoveries. The most famous one is "Move 37" —— a rare move made by AlphaGo during its match against Lee Sedol. According to analysis, the probability of making this move was extremely low, almost negligible, and under normal circumstances, no human player would have chosen it. However, upon reviewing the game, this move turned out to be an exceptionally brilliant strategy.
This case fully demonstrates the potential of reinforcement learning: AlphaGo discovered strategies unforeseen by humans through continuous self-play, achieving incredible success.
Breakthroughs in Reinforcement Learning and Reasoning Ability
The power of reinforcement learning is not only reflected in its ability to surpass human levels in Go but also in its capacity to provide new ideas for solving more complex problems. We are gradually applying this learning method to large language models (LLMs) to break through traditional reasoning problem-solving methods.
Unlike Go, the application domain of language models is much broader. It must not only handle structured tasks but also possess more complex reasoning abilities. By setting diverse practice questions and problem environments, the model can continuously refine its thinking patterns across different fields, and may even create new ways of thinking that humans have yet to imagine.
Beyond the Boundaries of Language: New Thinking and Language
The breakthrough of reinforcement learning in reasoning may go beyond the familiar framework of language. In the future, a completely new language might emerge, enabling more efficient thinking and reasoning, potentially surpassing the limitations of English or any existing language. Models could develop their own "language for reasoning" according to need, further enhancing their thinking ability.
This is precisely the frontier of current large language model research. Scientists are creating richer and more diverse "practice questions" to provide multi-domain thinking challenges for systems, helping them grow continuously in open-minded environments.