Research taste is a skill nobody talks about. How do you develop it without collaborators? [D]

**What it is:** A Reddit discussion post outlining a heuristic framework for developing "research taste" in ML — specifically, a four-step problem selection process that prioritizes trying simple baselines (including prompt-based solutions) before pursuing complex research approaches. **Why it matters:** The core argument is that problem selection skill is more valuable than technical execution ability, yet it receives far less explicit attention in ML education or practice — the post identifies two failure modes: over-engineering simple problems and pursuing problems the field lacks tools to solve. The practical challenge raised is that this judgment is typically learned through mentorship and peer feedback, leaving solo practitioners or those without strong research networks without a clear development path.