Google Nested Learning

Diff

Google's Nested Learning and Meta's (not Google's) Sparse Memory Finetuning are two distinct approaches to the problem of continual learning in AI, aiming to prevent "catastrophic forgetting". Nested Learning is an architectural paradigm, while Sparse Memory Finetuning is a specific training method within existing architectures. 

Google Nested Learning
Nested Learning is a novel architectural paradigm that treats a single model as a system of interconnected, multi-level learning problems that are optimized simultaneously at different rates. 
Core Concept: It introduces a Continuum Memory System (CMS), a spectrum of memory modules updating at different frequencies.
Mechanism: It uses various "layers" of memory:
High-frequency layers update often, storing recent, fast-changing information (short-term memory).
Low-frequency layers update rarely, storing stable, core knowledge that shouldn't change easily (long-term memory).
Result: This structural approach allows the model to naturally integrate new information without overwriting old knowledge, effectively giving the AI "neuroplasticity".
Proof-of-Concept: Google developed an architecture called HOPE (a Self-Referential Learning Module with Continuum Memory) to implement these principles. 
Sparse Memory Finetuning
Sparse Memory Finetuning, developed by Meta AI, is a training method that works within existing memory-augmented transformer architectures to update only the most relevant parts of the model's memory when learning new facts. 
Core Concept: It leverages the inherent sparsity of memory layers, where only a small subset of "memory slots" are activated during a given operation.
Mechanism: When new data is introduced, the method identifies and updates only the specific memory slots highly activated by that new information relative to pre-existing knowledge.
Result: This highly targeted approach learns new knowledge effectively while drastically minimizing the degradation of held-out knowledge, reducing forgetting to a much lower degree compared to full finetuning or LoRA.


SCMS is highly practical, workflow-centric, empirically proven, and focused on actionable session memory management for dev environments (cursor, VS Code, etc.). It achieves continual learning by letting users validate, update, and reapply patterns without having to retrain models—or rely on external memory/databases.

Nested Learning by Google is a paradigm shift: it views models as nested sets of optimization problems (not just layers). Continuum Memory System treats memory as a spectrum of modules, each with a custom update rate. This creates multi-timescale learning (short/medium/long-horizon), stops catastrophic forgetting, and unifies architecture and optimization under “associative memory” principles. Google’s approach is more theoretical but solves deep continual learning, recurrent and transformer memory, and self-modifying network problems in one framework.​

In short:

SCMS: Practical, workflow-embedded, open-source, session pattern memory for LLMs.

Nested Learning: Fundamental, theoretical architecture redesign for all neural models—continuum spectrum, multi-frequency modules, no dichotomy between memory types, powerful for long-context, lifelong continual learning.

Both aim to reduce cost and improve memory in AI, but SCMS is a developer workflow toolkit while Nested Learning redesigns the core memory update mechanics in deep learning itself.

FB sparce memory finetuning

SCMS (Sparse Contextual Memory Scaffolding), a new architecture to enable continual memory for AI workflows. SCMS helps LLMs (like Claude, GPT, etc.) retain and reapply patterns in development without retraining or external vector databases.

Key benefits:

96% reduction in repeated pattern reimplementation time.

Optimized for long-horizon coding tasks.

Fully model-agnostic, adaptable across AI tools and IDEs (Windsurf, Cursor, VS Code).

Developed from a 4-month empirical study and released open-source ([GitHub Starter Kit & Whitepaper link https://github.com/AIalchemistART/scms-starter-kit]).

Addresses two main AI productivity barriers: cost escalation as projects grow, and AI forgetting (catastrophic forgetting with traditional fine-tuning, up to 89% drop in retained knowledge).

SCMS uses a layered memory approach (inspired by Google and Meta research): targeted updates ("memory surgery") avoid overwriting, and separate short- and long-term memory layers mimic human memory, allowing durable and reusable knowledge.

Economic impact:

Real-world case study showed 53% reduction in tokens needed per interaction, saving ~$660/year for individual heavy AI devs and millions at scale.

Even with zero retrieval, SCMS forces clearer standards and context, proving that token quality is more important than quantity.

Systematic session closure (reviewing and validating AI patterns at the end of workflows) creates a compounding asset of intelligence, with high ROI for invested refinement time.

Ultimately, SCMS offers a scalable, open-source framework for sustainable AI development, democratizing access and reducing costs, raising the question of what new problems can be solved as the cost of trustworthy AI plummets.

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Filing Cabinet vs. Validation Pipeline Metaphor:

explanation video strongly contrasts the old "digital attic"/filing cabinet model (where users dump preferences and details for passive storage) with the new validation pipeline paradigm. SCMS is explained as a system that tests and validates patterns, only retaining those that prove useful in practice, like an automated test suite for knowledge—explanation analogy is more prominent and elaborated here.

Quantitative Survey Insights:

Unique charts and data are shown about memory usage:

87% of what users "save" are just simple preferences, and only 22% is reusable knowledge.

Only 35% of memories are ever re-used, leading to digital clutter.

Over 60% of users report not knowing what they've told their AI to remember, highlighting write-only memory problems.

explanation granular survey-based critique of traditional approaches is more detailed here, with explicit numbers and user behavior analysis.

Story of Accidental Discovery:

Greater narrative focus: the origin of SCMS is framed as a discovery from a single developer's real-world frustration, evolving naturally into an empirical breakthrough (rather than as a product from the outset).

Stepwise Breakdown of SCMS:

Four main steps of the SCMS process are explicit:

New ideas/patterns go into a temporary test layer.

Only those validated by repeated use are promoted.

Battle-tested patterns are added to a permanent memory that must always be checked.

Unused patterns self-prune ("self-cleaning system").

The test suite/natural selection metaphor for memory is unique in how it’s conveyed here.

Impact Data and Generalization:

More specific empirical results:

91% reduction in rediscovery time,

154% improvement in knowledge retention,

98% reduction in documentation lag.

Broader application: SCMS results are highlighted across domains (scientific research, content creation, data analysis) with domain-specific percentages. (E.g., 81% reduction in debugging time for analysts)

Discovery Gap vs. Adoption Crisis:

Novel conceptual point: The problem isn't that users refuse to adopt SCMS—it's that fewer than 1% even know about explanation validation-first method, due to current tool design. explanation "discovery gap" concept is highlighted here to explain low natural adoption.

Mandatory UX Proposal:

explanation video proposes that the UI itself should guide users to categorize memory as preference vs. testable pattern, pushing for "mandatory UX" built into AI tools—a usability argument not emphasized in the previous video.

Broader Message on AI Progress:

Ends on the claim that future AI progress may be less about bigger models and more about active, smarter memory systems like SCMS that convert passive memory into validated learning.

In short:
explanation video frames SCMS as a paradigm shift using vivid metaphors and survey data, details its origin as a user-driven discovery, formalizes its operation as a "test suite" for knowledge, and issues a call for tool support to make explanation approach the default. These narrative, UX, and behavioral insights are more detailed and explicit here compared to the previous (more economic/engineering-focused) explanation.

Google Nested Learning

Google Titan Paper: https://gist.github.com/rsrini7/54d0517ce823746b7a5f264a644be7b5

Problem Context: Catastrophic Forgetting

In typical machine learning, especially large language models (e.g., GPT), when you fine-tune for a specific domain (e.g., finance), the model becomes better in that area but tends to forget general knowledge learned earlier.

This issue is called catastrophic forgetting: the model overrides old knowledge rather than integrating new info with past learning.

Current Solutions and Their Limitations

Approaches like replay buffers or architectural tweaks (commonly used in reinforcement learning) are only patchwork fixes.

These do not fundamentally address how models update and integrate new knowledge without overwriting prior information.

Machine learning models "forget" unlike the human brain, which can integrate new learning with old knowledge.

Google Nested Learning Approach: Structural Rethink

Not just an architecture tweak, but a new foundational learning principle for ML models.

Nested Learning uses multiple levels of abstraction and interconnected optimization stacks—essentially, the optimizer and weights are organized in a nested/hierarchical way.

Model, optimizer, and data are connected such that the optimizer and weights are "nested" within each other (unlike traditional setups).

Learning Across Multiple Abstraction Levels

Different model layers are trained at different update rates; some layers learn faster, others adapt slower, mirroring learning diversity in the brain.

Example: Early layers might rapidly adapt, middle layers are slower, and some parts might specialize independently.

Russian Doll Analogy

The system is like Russian dolls—nested learning modules with different update intervals and levels.

This mimics how the human brain learns: through multiple time scales and memory hierarchies.

Continuum Memory System

Google’s Nested Learning introduces a continuum memory system—distinct memory modules (fast, medium, slow), each updated at different frequencies.

For a deep neural network, groups of layers (early, middle, late) update at their own rates, storing information as fast, medium, and slow memory.

Optimizers as Memory Modules

Optimizers transition from mechanical to associative structures, acting as trainable memory modules that can recall examples, not just update weights.

HOPE—Self-Modifying Architecture

Google released a new architecture called HOPE (as an evolution of its Titan models).

HOPE is a recursive learning architecture with self-modifying learning rules, long context windows, and more deeply nested memory.

It uses container memory systems to support these features and extends the previous Titans model.

Experimental Results

HOPE outperforms all earlier benchmarks: achieves lower perplexity (better text predictions) and higher reasoning accuracy than Titans, Sambar, and transformers.

Practical examples include: chatbots that adapt to new domains without forgetting general knowledge, and layers with both fast and slow memory for improved adaptation.

Conclusion

Google Nested Learning represents a structural rethink of how AI models learn—aiming for continual evolution without forgetting.

It paves the path for next-generation, self-modifying, and lifelong learning AI systems that better mimic human-like learning and memory integration.