The Signal

Everything that matters in AI, with our take.

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arXiv cs.AIPaper7d ago

Multiscale Policy Optimization for Long-Horizon LLM Agents

Long-horizon agent training is hard and most RL approaches treat every token equally. This observes that value varies across turns not tokens, so you can improve credit assignment by training at turn scale with token-level details. Results across search, tool-use, and automation tasks show consistent gains with 4B and 30B models. If you're training agents with RL, revisit your reward signal and credit assignment; this likely beats your current setup.

research agents rl reasoning

arXiv cs.CLPaper15d ago

Language Models Learn to Improve Language Models

This is one of the first credible attempts at automating LM improvement via reinforcement learning without prohibitive GPU costs. The surrogate feedback trick (using a frozen model to approximate what actual retraining would show) is clever enough to matter. For labs working on efficient model development workflows, this changes the math on whether model iteration can become genuinely algorithmic rather than handcrafted.

research labs rl model-optimization