Reinforcement learning
Reinforcement learning is a learning method that allows you to learn through trial and error by giving rewards if you do well and penalties if you do not. This is the core technique of AlphaGo and its inference model.
Reinforcement learning is a learning method that instead of directly providing the correct answer, rewards or penalties are given to the results of actions, allowing students to find better actions through trial and error. When teaching a dog how to sit, it is the same principle as repeating the training by giving it a treat when it does well, rather than explaining it with words.
It is difficult to produce correct answer data, but it has been developed to be suitable for problems in which good or bad results can be judged, such as games and robot control. It was the core technique of AlphaGo, which surpassed humans in the game of Go, and has recently been attracting attention again as the driving force of the inference model that trains LLM in tasks where the correct answer can be confirmed, such as mathematics or coding.
However, if the reward design is incorrect, reward hacking can occur in which the AI only collects points through unintended tricks, so deciding what to reward is considered the most difficult part.
✅ Why it matters
- Finds the optimal behavior through trial and error even without answer data
- It is a proven technique that has outperformed humans in games, robots, etc.
- It has reemerged as a key driver of the latest LLM development, such as inference models.
⚠️ Limits and debates
- If the reward is designed incorrectly, reward hacking occurs where one learns tricks that are different from the intention.
- Numerous trials and errors are required, which requires a lot of learning cost and time.
- When applied to the real world, the cost of trial and error is high, leading to a high reliance on simulation.