# My Current Approach in Learning and Experimenting with LLMs and Deep Learning

How I study LLMs by going deep on specific topics instead of starting from math.

- Published: 2026-03-18
- Modified: 2026-03-18
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- Canonical: https://aroramrinaal.com/ai/my-current-approach-in-learning-and-experimenting-with-llms-and-deep-learning
- X: https://x.com/arora_mrinaal/status/2034163061184139750

I do not know if it is right or wrong but my approach from the past month has been coming across a topic and then going deep into it.

For example I picked up the [OLMo 3 blog and went deep into it](https://x.com/arora_mrinaal/status/2024835177307140212). That was my first experience coming across the terms involved in the complete training lifecycle of an LLM, like Base Model, Thinking SFT, DPO, RL, SFT, cold start, etc.

Then I came across the famous [Anthropic distillation post](https://x.com/arora_mrinaal/status/2026314927200182588) and learned about the terms hard distillation and soft distillation. It is the fastest way to at least reach closer to frontier-level models if compute or capital is the bottleneck and also if you want to make a lighter smaller and cheaper version of your big daddy model.

I am actually liking this way much much better than starting from scratch at the Mathematics level because the breadth I was able to cover with this strategy in just a single month was very good.

Through the OLMo 3 blog by Allen AI I was able to get a rough idea how these AI labs go about things and the major steps involved when training an AI model the different phases pre-training mid-training and post-training.

The most interesting thing to me was the Thinking SFT part of it when we take a base pre-trained model and teach it to emit reasoning traces those think tags that you see the model do. Which led me to my first experimental project [Teach a 3B Base Model to Emit Reasoning Traces](https://media.aroramrinaal.com/ai/nanbeige-cold-start-sft) basically doing a cold start SFT with a targeted dataset on a base model using the LoRA technique.

I would love thoughts from the ***Actual researchers in Academia or Industry*** to see if this approach of trying everything first so that I can truly see what I actually like and then diving deep into it is the right one or not.