Research

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.