New Training-Free Method Boosts ANN-to-SNN Conversion for Continuous Reinforcement Learning

Global: New Training-Free Method Boosts ANN-to-SNN Conversion for Continuous Reinforcement Learning

Researchers have introduced a training-free technique called Cross-Step Residual Potential Initialization (CRPI) to improve the conversion of artificial neural networks (ANNs) into spiking neural networks (SNNs) for continuous control tasks in reinforcement learning. The paper, posted on arXiv in January 2026, addresses performance gaps that arise when existing conversion methods are applied to environments requiring fine-grained action selection.

Background

ANN-to-SNN conversion enables the deployment of energy‑efficient spiking models by reusing weights from well‑trained conventional networks. This approach is attractive for reinforcement learning because it avoids the costly and potentially unsafe process of training SNNs directly through interaction with an environment.

Problem Identification

Prior conversion pipelines have shown limited success on continuous control benchmarks. The authors attribute this shortfall to error amplification: minor discrepancies between the ANN’s continuous actions and the SNN’s approximated actions become temporally correlated across decision steps, leading to a cumulative shift in the state distribution and a pronounced drop in performance.

Proposed Solution

CRPI mitigates error amplification by carrying over residual membrane potentials from one decision step to the next. This lightweight, training‑free adjustment preserves information about previous activations, thereby reducing the temporal correlation of approximation errors without altering the underlying network architecture.

Experimental Evaluation

The study evaluated CRPI on a suite of continuous control benchmarks that include both vector‑based and visual observation tasks. The experiments integrated CRPI into established conversion pipelines and compared performance against baseline methods that lack the residual potential mechanism.

Findings and Implications

Results indicate that CRPI substantially recovers lost performance, narrowing the gap between converted SNNs and their original ANN counterparts. The authors suggest that continuous control represents a critical benchmark for ANN‑to‑SNN conversion research, highlighting the need for mechanisms that address temporally correlated errors. Future work may explore extending CRPI to other domains where fine‑grained action precision is essential.

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.