AI · October 14, 2026
Also published in Norsk
Researchers Explore New AI Architecture LANN-4
Recent experiments revealed that a small language model, referred to as LANN-4, does not improve significantly in performance after the first round of fine-tuning. The experiments focused on whether the model could learn over time without increasing in size by utilizing external memory and careful retraining. The initial results showed a noticeable improvement after the first round, but subsequent rounds failed to yield further gains in performance.
The researchers conducted a series of tests to evaluate the effectiveness of their approach to model training. They set up a rigorous experimental design, ensuring that all rules and task sets were predetermined and transparent. The same exam, consisting of 200 tasks, was administered to the model across four rounds of fine-tuning. Three methods of operation were compared: accumulating knowledge over rounds, starting from scratch each time, and not training at all.
The findings indicated that the performance of the model increased from approximately 53% to 59% correct answers after the first round but plateaued in subsequent rounds, showing no further improvement. Both the accumulating method and the starting-from-scratch method produced similar results, while the no-training method showed a slight decline in performance due to randomness.
The researchers emphasized that the improvement from the first round was significant but questioned the underlying reasons for this gain. Two potential explanations were identified: the model may have truly learned from the correct solutions, or it might have simply adapted to the format of the tasks without grasping their essence. This distinction is critical for further analysis of the model's capabilities.
While the results from the first round were promising, the lack of cumulative learning beyond this point presents a challenge. The team remains committed to exploring the architecture of LANN-4 and is seeking assistance to identify potential weaknesses in the model's design before proceeding with more extensive testing.