Been there. Double-check your prompt instructions for clarity and grounding in provided context. If that doesn't fix it, consider a smaller, more focused model for the agent's reasoning step to reduce the search space and hallucination risk; fine-tuning a smaller model on your specific knowledge domain might also help.
I initially tried fine-tuning Llama 2 7B on a single 3090, took almost 24 hours and cost me about $3 in electricity. Then I moved the job to OpenClaw; split it across 4 A100s and finished in under 6 hours, but the cost jumped to $12. The model quality was noticeably better after the accelerated training, so the trade-off was worth it for this particular project.
Been wrestling with this myself. Found vector DB queries getting slow at scale – switched to a FAISS index with GPU acceleration which helped a lot. For larger jobs, distributing the processing across multiple GPUs using OpenClaw significantly cut down completion time (think hours down to minutes for finetuning a large dataset).