PACE: Two-Timescale Self-Evolution for Small Language Model Agents
Researchers have developed PACE, a novel framework for enabling small language model (SLM) agents to self-evolve without requiring model weight updates or access to frontier models. This two-timescale approach separates prompt refinement from control-logic updates, allowing for more robust and efficient agent development under resource constraints. In evaluations across various SLM backbones and benchmarks, PACE demonstrated significant performance improvements over existing methods, suggesting a viable path for deploying capable SLM agents in production environments. AI
IMPACT Enables more efficient development and deployment of capable language model agents using smaller, more accessible models.