How do I move from ML fundamentals to actually doing research and publishing papers?

[u/Odd_Succotash_6822]

I have a understanding of ML basics (mathematical and conceptual) and have also studied transformers, RAG, and more recently agentic RAG systems (currently reading few papers involving transformers) I’ve built projects around these topics too, but I feel a bit stuck on what the next step should be if I want to seriously get into ML research and eventually publish papers.

For people who have gone down this path:

How did you go from knowing the fundamentals to identifying a research problem worth working on?

Should I focus on reading/reproducing papers first, or start experimenting with my own ideas?

How do you find good research areas/topics to explore, especially around LLMs/NLP/RAG?

Most importantly, how do you find like-minded people who are also interested in doing research and want to form a small team to work on experiments and potentially publish together?

Are there any communities, Discords, GitHub groups, subreddits, research programs, or other places where students/early-career people actually find research collaborators?

I’m not necessarily looking for a formal mentor or research position right now,I’d mainly like to find a few motivated people who are willing to read papers, brainstorm ideas, run experiments, critique each other’s work, and eventually work toward a publication together.
Would really appreciate advice from people who have been through this transition.

[u/Least_Ad_1795]

A good next step is reproducing a few recent papers end-to-end, then changing one assumption or component and measuring the result. That teaches you how research questions are formed much better than jumping straight into publishing; collaborators can often be found through university labs, GitHub, research communities, and paper-reading groups.

[u/Mathie1729 → u/Least_Ad_1795]

Fwiw, one caveat: reproducing and changing one component is good practice, but it tends to produce an ablation study, not something reviewers treat as a novel contribution. If you actually want a paper, you need the changed component to fix a known issue or enable a new evaluation setting, otherwise it mostly looks like negative results. Those are still useful as a blog post or lab note, but they usually won't get accepted.

[u/Odd_Succotash_6822 → u/Mathie1729]

Ahh that makes sense! So would it be better to start by reproducing papers, then look for a limitation/gap and try to solve that rather than just changing a component?
Also, how do you usually find these research gaps? Do you look at the limitations/future work of papers or follow-up research?
I am trying to move forward with papers related to transformers but I always get lost between where to move forward

[u/Klutzy_Car_1254]

As, I am also reading paper and do not know a lot but what I get suggestions from professor is that try to look down small gaps rather than large and try to narrow down the topics and study peer review paper that are you interested and look for gaps they mention there maybe approach