More than a year ago, I shared how liquid neural network can be an answer to some challenges that conventional LLMs may run into in the enterprise planning context. https://lnkd.in/gfVh8pfP

This new approach (link below), SEAL, is another take on self-adapting LLMs, from the same source (MIT CSAIL) but with a different core idea..

If it is robust and scales well, this may exactly be the kind of LLM that many enterprise planning applications may need.

A challenge with conventional LLMs is that once trained and deployed, they have static weights and cannot truly “remember” new information shared after deployment.

In the SEAL approach, the model creates synthetic “study sheets” from user inputs by rewriting and summarizing information. It tests multiple self-edits (internal updates), evaluates which improves performance, and uses reinforcement learning to choose the best update.

It then updates its own weights accordingly, internalizing the knowledge.

When it comes to the core idea, there is a fundamental difference between this approach and liquid neural networks.

Liquid neural networks use differential equations to make neuron states evolve continuously over time, where each neuron behaves like a “liquid” dynamical system. Adaptation is intrinsic, i.e, neuron dynamics respond instantly to new inputs (no retraining).

This makes them good candidates for real-time, continuous-input tasks (e.g., control systems, robotics, autonomous driving).

The SEAL method lets a pre-trained LLM generate its own synthetic “study sheets” and self-edit its internal weights using reinforcement learning. Adaptation is explicit, i.e, the model evaluates self-edits and updates its weights to permanently store new info.

So looks like SEAL has been designed for symbolic / knowledge tasks, e.g., learning facts, reasoning, or improving language skills.

An interesting read to start the day! https://lnkd.in/gjFjm4ax