You've misunderstood the bitter lesson if you think it applies here. It's not "frontier LLMs are always better", it's "algorithms that scale with compute/data will usually end up surpassing algorithms that are based on human understanding".
Fine-tuning an open-source LLM with reinforcement learning in order for it to be better at a specific task does not go against the bitter lesson at all.
You've misunderstood the bitter lesson if you think it applies here. It's not "frontier LLMs are always better", it's "algorithms that scale with compute/data will usually end up surpassing algorithms that are based on human understanding".
Fine-tuning an open-source LLM with reinforcement learning in order for it to be better at a specific task does not go against the bitter lesson at all.