Abacus.AI applied the same fine-tuning recipe to three very different open-weight models. The gains landed in completely different places & that tells you more than any single benchmark score.
In all three cases, the gains concentrate where the base model's gaps were widest. Same recipe, different floors.
An AI agent could provide an output without completing the task. In real-world scenarios, there is a need for the agent to comprehend the user query, identify an appropriate tool, analyze output, and take subsequent actions. After some number of cycles, the model would veer away from the initial goal, thus compromising on the credibility of the result.
It presents a problem for teams developing reliable agents. While a chatbot provides a response to a single request, an agent has to retain its context and make decisions based on related actions. With the extension of workflow, there would be additional cost implications as well as chances of mistakes.
Source: KDnuggets — Read the full article on the original site ↗