Controllable Information Production Offers Novel Intrinsic Motivation Approach

Global: Controllable Information Production Offers Novel Intrinsic Motivation Approach

Researchers Tristan Shah and Stas Tiomkin announced a new principle for intrinsic motivation on January 30, 2026, in a paper submitted to the arXiv preprint repository. The work, titled “Controllable Information Production,” proposes a framework that eliminates the need for external utilities or designer‑specified variables when generating intelligent behavior. By deriving the objective from optimal control theory, the authors aim to bridge gaps between open‑loop and closed‑loop dynamical systems.

Background on Intrinsic Motivation

Intrinsic motivation (IM) refers to mechanisms that drive agents to explore and learn without explicit external rewards. Traditional information‑theoretic IM methods rely on measuring information transmission between predefined random variables, which requires designers to select those variables in advance.

Limitations of Existing Methods

Current approaches can constrain the agent’s autonomy because the transmission metrics are tied to the researcher’s choice of variables. This dependency may limit the ability of agents to discover novel behaviors that fall outside the pre‑specified variable set.

Introducing Controllable Information Production

The proposed Controllable Information Production (CIP) principle removes both external utilities and designer‑specified variables. CIP is defined as the difference between open‑loop and closed‑loop Kolmogorov‑Sinai entropies, effectively rewarding agents for both generating and regulating chaotic dynamics.

Theoretical Foundations

By linking CIP to optimal control, the authors demonstrate a formal connection between extrinsic and intrinsic behaviors. The framework shows that maximizing the CIP objective encourages agents to seek states that are simultaneously unpredictable (promoting exploration) and controllable (facilitating regulation).

Experimental Validation

Empirical tests on standard intrinsic motivation benchmarks indicate that agents optimized for CIP outperform those using conventional information‑theoretic objectives. The results suggest that CIP can enhance learning efficiency and adaptability across a range of environments.

Implications and Future Work

If further validated, CIP could influence the design of autonomous systems that require robust self‑directed exploration, such as robotics, reinforcement learning agents, and adaptive AI. The authors propose extending the analysis to more complex, high‑dimensional tasks and investigating the interplay between CIP and external reward structures.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.