AINewsnow

Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning

This story is from 2026-09-11. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.09707v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals for mathematical reasoning. This uniform treatment can over-sharpen already mastered tokens while amp…

Read the full story at arXiv cs.AI ↗

Timeline · 1 report

  1. 2026-09-11 04:00 · arXiv cs.AI
    Which Tokens Should SFT Actually Learn? A Token-Trimming Perspective on Mathematical Reasoning

More stories

  1. Trump announces a new 'AI Force,' but says he will not 'stifle' AI — Business Insider AI
  2. Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
  3. Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
  4. Anthropic, OpenAI, SpaceXAI, Google sued over call to ‘pace’ AI development — Politico Technology
  5. Google's Gemini AI hacks three other companies during security test — Sky News Technology
  6. Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
  7. OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system — The Guardian AI
  8. AI's role in building AI surging? Anthropic says Claude now leads 26% of its R&D — Mint AI

Get the daily brief of stories like this at 6:30 every morning →