DeepMind and Princeton Propose TailSFT to Boost RL Performance

Researchers from DeepMind and Princeton propose TailSFT, a lightweight tail-weighted SFT method that improves coverage and yields better post-RL performance than standard cross-entropy SFT, making every costly RL step more effective.

2026-09-12 ~ 2026-09-12 · 3 related posts

• Princeton team's Coverage Principle paper explains why xent SFT underperforms for RL, proposes TailSFT — canondetortugas · 2026-09-12
• TailSFT: skip already-learned SFT examples to boost post-RL pass@k — canondetortugas · 2026-09-12
• TailSFT: tail-weighted SFT improves coverage and post-RL performance, says new paper — canondetortugas · 2026-09-12