LARA claims LoRA-equal quality from residual-stream adapters that stack many behaviors per token on one frozen model

GitHub / pfekin (via r/MachineLearning, 15 comments)·low signalXBlueskyLinkedInCopy link

Pfekin posted LARA (Lightweight Additive Residual Adaptation) to r/MachineLearning as a [P], pitching modular post-training for frozen LLMs: residual-stream adapters that match LoRA at equal parameter count, add inference-time steering, and run multiple behaviors per token on a single model without swapping weights. The repo (github.com/pfekin/LARA) was created 2026-07-06 and last pushed 2026-09-14 and sits at 3 stars, so there is no independent validation of the LoRA-parity claim yet. The many-behaviors-per-token property is the part worth checking if it holds, since it removes the adapter-swap cost from multi-persona serving.