Unverified50% confidenceFactExact time
The core pain point that Function Calling addresses is that large models enter a state of knowledge stagnation after training ends and cannot obtain new information after the training cutoff.
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Long-term
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9/17/2026
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UnverifiedAs the scale of pretrained models increases, many previously effective explicit knowledge injection techniques have gradually become ineffective or even produced negative effects.74% similarUnverifiedRecursive training makes extensive use of task completion rates and code execution results as reward signals, lacking explicit supervision for narrative consistency, which may lead to catastrophic forgetting of general language modeling capabilities, and this is more pronounced in models with small parameter counts.74% similarUnverifiedCurrent large models are essentially static—once training is complete, the weights are frozen and knowledge remains at the training cutoff date.73% similarVerifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.70% similarUnverifiedSimply stacking the number of RL tasks does not lead to better training outcomes, and unsolvable tasks instead drag down overall training quality.69% similar
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