This idea is called Memory Transfer Learning (MTL).
A large memory pool is assembled from different types of development tasks, after which the agent reuses this memory across domains.
→ Such memory becomes a common resource and a universal library of experience for many agents and models.
An increase (+3.7% on average) is achieved due to meta-knowledge:
* how to validate a solution
* how to structure debugging
* what checks to run
* how to detect failure patterns
At the same time, the level of abstraction is important: memory that is too tied to a specific task degrades quality.
Memory for debugging, code generation, and testing is combined into a single common pool. The more memory, the better the transfer works.
MTL enables the agent to reuse general reasoning and checks, not just exact solution paths.
GitHub, Article
••••••••••••••••••••••••••••••••••••••
🤖 Data & ML | @DataXplore