Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models

AI Digest - ArXiv AI

Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models

How can we predict which base checkpoint is worth an expensive round of agentic post-training? End-to-end pass@$K$ tests whether successful behavior already appears in a base model's distribution, but it is a poor fit for agentic coding: many base checkpoints cannot reliably produce the well-formed tool invocation required to complete a task end-to-end. Single-shot or short-horizon tasks avoid these tool-calling failures by collapsing a multi-step interaction into a fixed prompt and a single pat

Source: ArXiv AI