Treat continual learning as representational-form coevolution

Type: kb/types/note.md · Tags: learning-theory, deploy-time-learning

Continual learning requires governing behaviour-changing writes, not just storing content argues that continual learning requires governing durable behaviour-changing writes — system-definition writes — beyond storing content, and that a deployed system's writable surface may include three representational forms (how an operative part is encoded and consumed): distributed-parametric (weights, adapters, embeddings, learned controllers), natural-language (prompts, notes, specs, rubrics), and symbolic (code, schemas, tests, tools). That note defers the general cross-form trade space to this one. Retained system-definition artifacts enable persistent deployment-time adaptation, so the readable forms already adapt durably during deployment. How should their improvement loops relate? They aren't independent: optimizing one assumes a position about the others.

Natural-language and symbolic cluster as the readable artifacts - inspectable, editable, distinct from distributed-parametric state in inspection method and update cost. The practical question of where to start building automated loops is the readable-artifact loop is the tractable unit for continual learning. This note is about the generic coevolution frame.

A mainstream direction: scaling the opaque loop

Computer vision provides the model. Before representation learning, features (SIFT, HOG) were hand-crafted and classifiers (SVMs) were learned — a clean separation that looked normal. Representation learning won by extending gradient descent across both, end-to-end. The general method didn't change; it covered more of the pipeline.

Wikipedia's secondary summary of the bitter lesson describes a long-run tendency for computationally scalable methods such as search and statistical learning to outperform approaches based on domain-specific understanding, and reports Sutton's recommendation to prefer simple scalable methods over increasingly elaborate human insight. One linked Commonplace analysis offers a narrower, case-level conjecture: pressure may consume a hand-crafted component whose claimed scope was never earned. It explicitly does not establish that mechanism as a broad historical tendency. This note uses it only as a possible explanation for why tokenizers and test suites might survive pressure that consumed SIFT. The next step is likewise this note's extrapolation, not the source's claim: it reads RLHF, RLAIF, continual pretraining, online learning, and fast adapters as a mainstream attempt to extend the opaque loop far enough to subsume the hand-crafted prompts, tools, and evals that deployed systems depend on. This may or may not succeed; new architectures could close the tempo gap, or structural limits could keep large opaque updates on a slower cycle. This note takes no position on the outcome.

Loops today

Current methods range from single-form optimization to early cross-form loops:

  • DSPy, ProTeGi — automated search over prompts (natural-language), weights frozen.
  • Genetic programming, FunSearch — automated search over code (symbolic), weights frozen.
  • Meta-Harness — automated search over harness code and prompt/context logic (symbolic + natural-language), weights frozen, benchmark traces as selection signal.
  • Symbolic Learning — a prompted language loss produces textual gradients that guide updates to prompts, tools, and pipeline topology (natural-language + symbolic); re-evaluation with the language-based loss decides rollback, while model weights remain fixed.
  • Memento-Skills — deployment-time mutation of natural-language-plus-code skill folders around a frozen LLM, with a contrastive parametric router trained separately through single-step offline reinforcement learning.
  • Co-Harness — in a two-round experiment, validated repair of prompts, tool schemas, skills, middleware, and memory alternates with model fine-tuning on verified trajectories, spanning distributed-parametric weights plus a natural-language/symbolic harness.
  • RLHF / RLAIF — updates weights (opaque), treating prompts and code as fixed.
  • Hand curation (Commonplace and similar) — evolves natural-language fast and symbolic artifacts slowly, without automated search or weight updates.

The cross-form cases change the inventory, not the underlying difficulty. Symbolic Learning and Memento-Skills keep the main LLM fixed; Co-Harness spans all three forms but does not isolate coevolution from extra fine-tuning, improved trajectory data, or additional harness search. The open problem has moved from whether two or three forms can appear in one automated loop to whether cross-form credit assignment, compatibility, rollback, and validation can make the coupling reliably compound.

Difficulties

The three forms have very different dynamics:

  • Distributed-parametric updates via gradient descent or other numerical optimization. Needs differentiable or probe-derived signal and heavy training infrastructure; large updates cycle on days to weeks, though smaller add-on mechanisms can be faster.
  • Symbolic artifacts are mutated by LLMs or search, then evaluated by tests, execution, or formal checks.
  • Natural-language artifacts are mutated by LLMs and evaluated by execution, use, or LLM-as-judge. Semantics stay underspecified, so verification is softer.

A joint optimizer has to handle pace mismatch — either it runs at the slowest class's cadence, or classes coevolve asynchronously without diverging — and cross-class credit assignment: a deployment failure rarely says which class wants the update (prompt revision, tool extraction, memory promotion, weight update, retrieval change). Per-class methods sidestep both by fixing the class in advance.

Starting point

Coevolution is the right conceptual frame, but a three-way joint optimizer isn't the near-term plan. The readable-artifact loop is the tractable unit for continual learning argues for starting with the natural-language + symbolic pair, on the basis of structural couplings that make the two a natural joint target.

Relevant Notes: