TLDR: APTBench is a new benchmark designed to evaluate the agentic capabilities (planning, action, atomic skills) of base Large Language Models during their pre-training phase. It converts complex, multi-turn agent tasks from software engineering and deep research into multiple-choice or text completion questions, offering a more accurate and cost-effective assessment of a model’s potential as an agent compared to traditional benchmarks.
The rapid advancement of Large Language Models (LLMs) has led to the emergence of sophisticated LLM-based agents capable of performing complex real-world tasks. Traditionally, evaluating the capabilities of these models during their pre-training phase has focused on isolated skills like general knowledge, mathematics, or code reasoning. However, these conventional benchmarks often fail to capture a model’s true “agentic potential” – its ability to plan, act, and adapt in dynamic, multi-turn interactions.
Recognizing this critical gap, researchers have introduced APTBench, a novel framework designed to assess the agentic capabilities of base LLMs during their pre-training stage. This benchmark is crucial because a model’s core competencies are established during pre-training, and altering them later can be prohibitively expensive and often yields suboptimal results. By evaluating agentic potential early on, APTBench provides valuable guidance for model training, allowing researchers to make informed decisions about data mix and architecture design.
APTBench addresses the challenge of evaluating base models, which typically lack the instruction-following abilities required for full-scale, end-to-end agent evaluations. It achieves this by transforming complex, real-world agent tasks and their successful execution paths (trajectories) into more manageable multiple-choice or text completion questions. This innovative approach allows for a lightweight and cost-effective assessment of core agentic abilities such as planning and action, across key scenarios like software engineering and deep research.
The construction of APTBench involves several key steps. First, tasks and successful trajectories are collected from real-world agent applications. These trajectories, whether human-generated or from high-performing agents, are carefully validated. Next, agent-oriented questions are formulated by identifying core agent abilities: planning (overall and stepwise decision-making), action (executing the next step, like a tool invocation), and atomic abilities (scenario-specific skills such as bug location in software engineering or citation generation in deep research). Content related to these abilities is then extracted and converted into multiple-choice or text completion questions.
For answer generation, the correct next step from a successful trajectory serves as the ground truth. For multiple-choice questions, LLMs are used to create plausible but incorrect choices by degrading the correct answer, ensuring the correct option remains optimal. For concise answers, a text completion format is used. All questions undergo human validation to ensure accuracy.
APTBench is divided into two main scenarios: APTBench-SWE (Software Engineering) and APTBench-DR (Deep Research). APTBench-SWE includes tasks like environment setup (evaluating planning, action, and error handling) and issue fixing (evaluating planning, action, bug localization, fix patch selection, and test patch generation). APTBench-DR covers closed-ended questions (assessing stepwise planning and final answer generation) and open-ended questions (evaluating overall planning, report generation, and citation abilities).
A significant feature of APTBench is its reliance on long and multi-turn trajectories, which helps evaluate both agentic capabilities and long-context processing abilities. Experiments with various open-source models have revealed interesting insights. For instance, agent capabilities tend to emerge at a critical model size, with smaller models struggling to acquire these skills. The benchmark also highlights the crucial role of training data quality and task alignment, showing that models with similar architectures and parameter scales can have vastly different agentic performance based on their pre-training data.
Crucially, APTBench demonstrates a strong positive correlation with downstream agent tasks, unlike general benchmarks which often show weak or even negative correlations. This indicates that APTBench provides a more predictive signal of a model’s real-world performance as an agent. The benchmark also suggests that improving long-context handling is vital for robust agent performance, as removing long-context tasks can sometimes strengthen the correlation with downstream evaluations.
Also Read:
- Automated Peer Review for Large Language Model Evaluation
- FunReason-MT: Enhancing AI’s Ability to Use Tools in Complex Conversations
In conclusion, APTBench offers the first feasible and economic solution for evaluating the agentic potential of base LLMs during pre-training. It provides quantitative metrics that can effectively guide agent-oriented pre-training, fostering the development of more capable and autonomous LLM agents. For more details, you can refer to the full research paper: APTBench: Benchmarking Agentic Potential of Base LLMs During Pre-Training.
