Unfortunately the nature of this field is "the bitter lesson", scale trumps everything in machine learning so unfortunately/fortunately we are getting interested in language models when the scale is so large that it is impossible to make in impact on them unless you own your own $xxM company.

However, there are several interesting research avenues you can take:

1. Improve small models with RLHF + fast implementations for a specific task (e.g. llama.cpp)
2. Chaining models together with APIs to solve a real human problem
3. Adding multimodal inputs to smaller LLMs
4. Building platforms to make it easy to train and serve LLMs for many use cases
5. Analyzing prompts and understanding how to make the most of the biggest LLMs