finding

active

finding:representational-dynamics-of-causal-emergence-align-with-reward-improvement-in-most-tasks

Representational dynamics of causal emergence align with reward improvement in most tasks.

The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.

Source paper

extracted_from

The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents

(2026) · Federico Pigozzi · Michael Levin

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Hypotheses (1)

hypothesis

Causally Emergent Alignment Hypothesis
supports
The hypothesis that successful RL agents will display causal emergence that is predictive of final reward early in training and whose representational dynamics align with reward improvement.

Communities (2)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning and adaptation
members_of
Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Quotes (1)

quote

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.
about
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Causal emergence identification tasks can be understood as causal representation learning tasks.claim0.865
Authors propose a conceptual mapping between CE identification and CRL.
Representational dynamics aligned with reward improvement in most RL tasks.finding0.862
Secondary empirical result: CE-based representational changes correlate with task success.
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.finding0.851
Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.claim0.839
Cross-fertilization claim made in discussion.
Successful RL agents exhibit causal emergence that predicts final reward early in training and aligns representational dynamics with reward improvement.hypothesis0.832
Central finding: causal emergence serves as a previously undisclosed axis of neural representation reorganization in learning agents.
Causal emergence can enable causal interventions to create better RL agents.claim0.821
Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.
Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.820
Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
Empowerment as intrinsic reward bridges causal learning and reinforcement learning in agent development.claim0.819