arXiv Machine Learning
Generalized Residual Closure: General Learning Dynamics for Stability-Plasticity Compatibility
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arXiv Machine Learning
Aug 4
Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning
arXiv:2608. 01743v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central paradigm for large language model (LLM) post-training, but optimization toward new objectives can degrade capabilities already present in the base model.
arXiv Machine Learning
1d ago
Poincar\'e Meets Bellman: Revisable Memory, Operational Quotients, and Evidence-Supported Learning in Changing Environments
The paper presents a finite‑model synthesis that integrates operational state abstraction with optimal control within a stability‑evidence‑revision (SER) framework, termed "Poincaré meets Bellman." It distinguishes qualitative dynamics for reusable action‑response structures from dynamic programming that governs acquisition, retention, reuse, merging, and forgetting, and introduces a Bellman recursion over the joint law of hidden state and deployed memory. The authors derive explicit retention rules, demonstrate how factor sharing and informative observations improve identification, and verify coding and retention calculations through finite enumerations.
arXiv AI
Aug 25
Functional compatibility as a determinant of persistent neural learning
The paper demonstrates that functional compatibility—how well new learning can coexist with behavior that must be preserved—is a causal determinant of persistent neural learning. By deliberately altering compatibility between matched neural states, the authors show that persistent learning can be measured under a common retention requirement across different learning directions, architectures, and data modalities. The study reveals that learning rules vary in how efficiently they exploit compatibility, and that retention constraints and finite updates limit what can be stored, with nonlinear geometry ultimately preventing full compatibility realization.
arXiv AI
Aug 26
Revelation Control
Revelation Control studies how to price interventions that reveal hidden state only when the revealed distinctions can alter a consequential decision, while separately accounting for any useful progress the intervention itself creates. The authors develop a framework for learning systems that defines decision‑sufficient revelation, revelation depth, and a cost‑adjusted factorization criterion, and they provide a target‑independent protocol for model‑specific instantiation. Experiments on Qwen2.5‑7B and Mistral‑7B‑v0.3 show that deeper future‑learning probes have positive decision value and that productive reuse yields strict equal‑compute utility advantages, supporting a structural transfer of the decision theory and evaluation protocol across architectures.
arXiv AI
Jul 8
To Retain or to Adapt? Generalizing Continual Learning
arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.
arXiv Machine Learning
Aug 10
Learning Suffers More Than the Policy Class Under Partial Observability: A Closed-Form Analysis
arXiv:2608. 07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one.