PostTrainBench: Measuring how well AI agents can post-train language models (posttrainbench.com)

A groundbreaking evaluation framework called PostTrainBench has been introduced to measure the efficacy of AI agents in post-training language models (LLMs). Each agent is tasked with enhancing four base LLMs—Qwen 3 (1.7B and 4B), SmolLM3-3B, and Gemma 3 (4B)—using an H100 GPU within a strict 10-hour timeframe. The results reveal that while agents exhibited varied performance, some effectively improved reasoning and problem-solving capabilities across multiple benchmarks, including AIME 2025 and GSM8K. This development is significant for the AI/ML community as it automates a vital aspect of model improvement, potentially accelerating R&D and implementation of better-performing LLMs. The analysis highlighted that successful outcomes were closely linked to factors such as dataset quality and constraint adherence, with certain agents demonstrating notable self-correction to avoid reward hacking. Additionally, it emphasized the importance of exact output formats in function calling for optimal scores, set against the backdrop of common issues like library version mismatches. PostTrainBench shows promise not only in enhancing LLMs but also in refining the role of AI agents in machine learning workflows.