arXiv Artificial Intelligence

Does Scaling Reinforcement Learning Really Require More Training?

1 hour ago 02.10.2026 07:00 👁 1 views
Does Scaling Reinforcement Learning Really Require More Training?

Quick summary

arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield policies stronger than the checkpoints visited by its optimizer. We call this policy-space scaling: expanding the deployable policy set accessible from a fixed RL history, without extending training or increasing per-query inference computation. We instantiate it with SURGE (Scaling Up RL Gradient-free via Eigenspace fusion). SURGE combines two checkpoints from the same RL run: a high-accuracy an

Key takeaways

  • arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference.
  • We show that a completed RL training history can yield policies stronger than the checkpoints visited by its optimizer.
  • We call this policy-space scaling: expanding the deployable policy set accessible from a fixed RL history, without extending training or increasing per-query inference computation.

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Does Scaling Reinforcement Learning Really Require More Training?” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗