Resource-Aware Agentic RL
An agentic reinforcement learning setup where money itself becomes the primary input signal: each generated token, tool call, or computation incurs a cost, and the agent's goal is to maximize productivity and performance under a finite budget.
Tasks are indefinitely optimizable (e.g., "produce the fastest, most memory-efficient solution"), and evaluation measures both correctness and efficiency. By exposing the true economic cost of cognition, the agent learns to balance exploration and exploitation, prioritize tools, and allocate resources intelligently, leading to emergent behaviors such as:
- Adaptive tool use
- Self-evaluation
- Cost-efficient planning
This framework transforms intelligence into an economic process: $ in, productivity out; no heuristics, no manual tuning, just emergent intelligence shaped by resource constraints.