Dear Sudeep Debkumar ,

One of the most significant advantages of Reinforcement Learning is its scalability to complex decision-making tasks that involve multiple steps, variables, and potential outcomes. RL algorithms can handle high-dimensional spaces and make decisions that consider long-term outcomes.

Reinforcement learning (RL) is a basic machine learning paradigm that does not require the raw data to be labeled, as is required typically with machine learning. Reinforcement learning helps determine if an algorithm is producing a correct right answer or a reward indicating it was a good decision. RL is based on interactions between an AI system and its environment. An algorithm receives a numerical score based on its outcome and then the positive behaviors are “reinforced” to refine the algorithm over time. In recent years, RL has been behind super-human performance on GO, Atari games and many other applications.

https://professional.mit.edu/news/articles/reinforcement-learning-right-your-ai-problem

Regards,

Shafagat

Interesting question, Sudeep Debkumar ,

I consider Reinforcement Learning a good ML technique for LLMs because it is providing them with a system of rewards to make the LLMs learn from mistakes and keep the good results on track. Of course, it is also supported by Reinforcement Learning from Human Feedback (RLHF) because it is giving the right rewards to secure safe responses and to offer helpful responses for any questions given to the LLMs. Even when we give our insights about whether it was helpful or not, we are taking part in this RLHF, so we are contributing to these LLMs via this complex ML.

So, what I am trying to say is that it is a good tool where all of us are taking part in it — in any topic we are interested in — and feel free to ask.

The distillation, on the other hand, rather than expanding the LLM’s knowledge, is making it more flexible by creating a replica (student) that behaves just for some specific tasks from the LLM (teacher). Thus, this replica will perform autonomously, and the LLM will have more room to learn new things — leaving these students (from distillation) to perform those tasks that are correct, safe, and useful for users.

Additionally, in theory and in practice, student models can evolve further. If a distilled student model is later fine-tuned with additional data or optimized using techniques like Reinforcement Learning (including RLHF), it can eventually serve as a new teacher. This can create a cyclical or layered learning process where models improve in stages — a concept sometimes referred to as progressive distillation or teacher-student chaining.

From my point of view, it is a good, interconnected, and kind of symbiotic set of tools that are serving LLMs to be what they are nowadays. I have found your topic interesting for debate, thank you.

Sincerely yours,

Raúl Fernández

Reinforcement learning is not simply good or bad — it depends on the context. It can achieve impressive results in dynamic environments, but it also requires huge amounts of data and careful design of reward functions. When applied responsibly, it can be very powerful, but without oversight it may lead to unexpected or inefficient outcomes.