LIVE PULSE
3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src1.9 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.3 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.0 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.0 arXiv paper proposes emotion regulation framework for empathetic speech dialogue in audio-language models — 1 src1.0 Paper Studies Graph Matching Relaxations for Supervised Graph Prediction — 1 src1.0 arXiv Paper Proposes Framework for Cognitive Attribution in Acquired Representations — 1 src3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src1.9 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.3 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.0 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.0 arXiv paper proposes emotion regulation framework for empathetic speech dialogue in audio-language models — 1 src1.0 Paper Studies Graph Matching Relaxations for Supervised Graph Prediction — 1 src1.0 arXiv Paper Proposes Framework for Cognitive Attribution in Acquired Representations — 1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE
Log inSign up

per-token gating

topic1 events
papersSEP 12 04:00 UTC

arXiv paper proposes unified per-token gating family for on-policy distillation

A new arXiv preprint introduces a family of per-token gating methods for on-policy knowledge distillation that mixes forward and reverse KL losses. The authors argue that prior approaches such as EOPD and ToDi each rely on a single fixed gating signal, and their framework generalizes these with multi-channel and bias coefficients. The work is a methodological contribution aimed at improving how distillation losses are weighted per token during training.

distillation/arxiv/training/kl-divergence