Lets go through Paper of DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning – Day 80

Lets First Go Through its official paper of : DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning What Is DeepSeek-R1? DeepSeek-R1 is a new method for training large language models (LLMs) so they can solvetough reasoning problems (like math and coding challenges) more reliably. It starts with a base model(“DeepSeek-V3”) and then applies Reinforcement Learning (RL) in a way thatmakes the model teach itself to reason step by step, without relying on a huge amount of labeled examples. In simpler terms: They take an existing language model. They let it practice solving problems on its own, rewarding it when it reaches a correct, properly formatted answer. Over many practice rounds, it gets really good at giving detailed, logical responses. Two Main Versions DeepSeek-R1-Zero They begin by training the model purely with RL, giving it no extra “teacher” data(no big supervised datasets). Surprisingly, this alone makes the model much better at step-by-stepreasoning—almost like how a human can get better at math by practicing a bunch of problems andchecking answers. DeepSeek-R1  Although DeepSeek-R1-Zero improves reasoning, sometimes it produces messy or mixed-language answers.To fix that, they: Gather a small amount of supervised “cold-start” data to clean up its style and correctness. Do…