arXiv (OAI Expanded)
open access
SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models
Spiking neural networks (SNNs) offer a path to energy-efficient language modeling through sparse encoding and event-driven computation, but training capable spiking language models from scratch remains difficult. A practical alternative is ANN-to-SNN migration through knowledge distillation (KD), wh
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models#781142
- SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting#1129698
- On the second-order optimization for spiking neural networks#1077308
- Causal pieces: analysing and improving spiking neural networks piece by piece#1141412
- Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams#1109733
Record · ID 779767
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive