ASPECT: Agent-Specific Parameter-Efficient Core Tuning for Multi-Agent LLM Workflows

Large language model multi-agent workflows increasingly use reinforcement learning to optimize specialized agents toward a shared system-level objective. This raises a fundamental question of what should be shared across agents and what should remain agent-specific. Existing parameter-sharing strategies either force multiple agents to update the same adaptation parameters or separate their adaptations entirely, creating a tension between parameter reuse and individual adaptation. To this end, we propose Agent-Specific Parameter-Efficient Core Tuning (ASPECT), which separates shared task-aligned adaptation structure from agent-specific parameter updates. ASPECT constructs a shared adaptation space from task calibration data and assigns each agent a compact private core within this space. This design allows agents to reuse common task structure while retaining distinct parameter adaptations and workflow-level coordination. Extensive experiments on Math and Code tasks demonstrate that ASPECT achieves a stronger performance–parameter efficiency trade-off than representative parameter-sharing baselines, with over 90% fewer trainable parameters.