Whose Turn to Adapt? Dynamically Ranked Sparse Low-Rank Adaptation for Onboard Satellite Tasking

Earth-observation satellite clusters increasingly rely on heterogeneous multi-agent reinforcement learning policies to schedule tasks and allocate resources, yet these policies are trained offline and flown frozen, leaving them pursuing outdated goals whenever mid-mission priority shifts outpace ground-station uplink win- dows. Adapting avoid this delays,low rank adapter (LoRA) finetune small portion of parameter locally reducing computational burden. However, exisitng rank al- locators measure importance from parameter on a fixed calibration set, treating as a single network in isolation and enforcing sparsity by masking, still carrying full-sized tensors that are infeasible for resource-constrained satellites. We pro- pose DRS-LORA, an onboard Dynamic Ranking based Sparse LoRA method that prunes rank directions by the singular value decomposition of each adapter’s out- put drift to train and fientune extremely few paramter when performance degrades when new tasks arrives. Across satellite agents, the budget is split by each agent’s contribution to the HAPPO advantage decomposition, a signal the policy update already computes. Within each agent, rank directions are ordered by the singular value decomposition of the output drift the adapter induces, so truncation gives the minimum output error for that rank. On a simulated four-satellite heterogeneous cluster with adaptation onboard through Jetson Orin Nano, DRS-LORA takesd only 13.7% of the joint policy trainable parameter during transfer learning on new tasks, retaining 92.1% of the full fine-tuning target completion rate.