Posts

This post establishes a formal mathematical framework for language agents, deriving fundamental challenges from first principles and providing concrete design guidelines with real-world examples from SWE-Bench.

An information-theoretic analysis showing that scalar advantage formulations learn ≤ log₂(B) bits per episode, while per-timestep advantages preserve full reward entropy.

The relentless push for faster inference creates a dangerous training-inference mismatch that silently kills RL with LLMs. We reveal the vicious cycle—particularly acute in reasoning and agentic RL—and show that sequence-level importance sampling is the principled solution.

Definitive mathematical formulations for rollout correction methods in VeRL, progressing from REINFORCE to PPO to Decoupled PPO. Handles policy mismatch, temporal lag, replay buffers, and off-policy algorithms with importance sampling and rejection sampling techniques.