Skyfall AI Releases MORPHEUS: A Persistent Enterprise Simulation Benchmark That Makes Continual Reinforcement Learning Necessary Under Structured Non-Stationarity

Most reinforcement learning benchmarks reset the world after every episode. Real operations never reset. Skyfall AI’s MORPHEUS targets that gap. It is a persistent enterprise simulation platform for continual reinforcement learning (CRL).

MORPHEUS is grounded in the Big World Hypothesis (Javed & Sutton, 2024). It says the world’s complexity exceeds any agent’s representational capacity. As a result, the environment looks non-stationary even under fixed dynamics.

To force continual learning, MORPHEUS requires three properties: persistence, non-stationarity, and operational complexity. Persistence means past decisions compound into future dynamics. Non-stationarity means any fixed policy eventually becomes suboptimal. Operational complexity means no fixed optimal policy exists.

Each environment is a self-contained TypeScript world plugin. It exports Operational Descriptors (ODs), a simulation scheduler, seed data, and documentation. An OD defines the step-by-step execution plan for a capability. Agents act through a capability API, and each call triggers an OD execution.

Building on that architecture, non-stationarity comes from two engines. First, a failure injection engine inserts typed disruptions between OD steps. It draws from eleven failure types, including missing_data , dependency_failure , and rate_limit . It runs at four preset rates: light (5%), realistic (8%), moderate (15%), and aggressive (30%).

Second, an asynchronous configuration shift controller changes failure presets and demand at fixed timestamps. It runs independently of the training loop, so shifts never align with gradient updates. This stops the agent from using update periodicity as a proxy clock.

Alongside these engines, reward comes from three operational verifiers logged natively by the platform. These are failure event signals, financial ledger status, and resource throughput. The composite reward combines them. Default weights are w_f = 0.5 and w_l = w_p = 0.25 .

Under the upper-bound assumptions (zero failures, minimum cost, full throughput), the bound per configuration equals 0.50.

Because the action space is large, pure RL from scratch is impractical. Therefore MORPHEUS uses a two-stage pipeline. A frontier model (Gemini 3.1 pro) collects trajectories using the ReAct framework. These traces then fine-tune Qwen3-14B via supervised fine-tuning (SFT).

Consequently, every RL run starts from this shared SFT checkpoint. This isolates continual learning behaviour from basic operational competence. All baselines then use PPO as the base optimizer for online post-training.