AI Training Algorithms' Irreversibility Framework Established
A new general framework has been established by researchers to define and analyze the irreversibility associated with AI training algorithms, which involve far-from-equilibrium dynamic processes. Their findings reveal that four different aspects of irreversibility—numerical backward error, time-renormalized correction, microscopic time reversal asymmetry, and stochastic-thermodynamic entropy production—are equivalent at leading order concerning step size η. This irreversibility leads to the emergence of a force that breaks time-reversal symmetry, disrupting non-isometric continuous reparametrization symmetries while maintaining orthogonal symmetries. This research is documented in paper 2605.21933 on arXiv.
Key facts
- Four characterizations of irreversibility are equivalent to leading order in step size η.
- Irreversibility breaks non-isometric continuous reparametrization symmetries.
- Irreversibility preserves orthogonal symmetries.
- The framework covers numerical backward error, time-renormalized correction, microscopic time reversal asymmetry, and stochastic-thermodynamic entropy production.
- Training algorithms introduce far-from-equilibrium dynamical processes.
- The work is a fundamental step towards understanding learning dynamics of modern AI systems.
- The emergent force is time-reversal-symmetry-breaking.
- Paper published on arXiv with ID 2605.21933.
Entities
Institutions
- arXiv