Empowerment as intrinsic reward bridges causal learning and reinforcement learning in agent development.

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Communities (3)

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Causal emergence in biological systems
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Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning agents
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Uses effective information (EI) and coarse-graining to link causal emergence with RL and biological learning.
Causal emergence in learning and adaptation
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Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Vectors (1)

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Care as the Driver — SCI Framework
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Source docs (1)

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2026-05-14_phil-trans-A-goodfire-aboutblank-impact.md
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Related by similarity (8)

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successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.831
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.
Representational dynamics of causal emergence align with reward improvement in most tasks.finding0.819
The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.
Empowerment-as-intrinsic-reward (Yiu-Allen-Ginosar-Gopnik) will be formally integrated into About Blank's SCI (Stress → Care → Intelligence) loop within 6 months.prediction0.812
Reinforcement learning acting on individual characteristics affecting their connections to others can result in dynamics that are equivalent to unsupervised learning at the system scale.claim0.806
Key insight linking individual rewards to system-level learning.
The elimination of reward as a motivator of behavior with prior beliefs dissolves the tautology of reinforcement learning (rewards reinforce behaviors that secure rewards).claim0.802
§4 Discussion.
Successful RL agents exhibit causal emergence that predicts final reward early in training and aligns representational dynamics with reward improvement.hypothesis0.798
Central finding: causal emergence serves as a previously undisclosed axis of neural representation reorganization in learning agents.
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.finding0.796
Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Reinforcement learning is sufficient for agency.claim0.795
Argument that RL meets the agency indicator.

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Empowerment-Based Approachframeworks/empowerment-based-approach.md0.788