End-to-End Method Gauges Agentic Capabilities Prior to Post-Training
agents training
| Source: HF Papers | Original article
Researchers propose an end‑to‑end method to assess agentic capabilities of large language models before post‑training, addressing the limited visibility of static or likelihood‑based evaluations.
A new research paper proposes a way to gauge the “agentic” abilities of large language models (LLMs) before they undergo the usual fine‑tuning stage. The study, authored by Jiarui Qin, Junjie Huang and Yunjia Xi, introduces an end‑to‑end framework called ASAP (Agent Scaffold Adaptation for Pretraining). Rather than relying on static, trajectory‑derived tasks or likelihood‑based proxies, ASAP evaluates a model through its native completion interface, observing how it behaves in interactive environments straight from the pretraining phase.
The authors argue that existing benchmarks give only a limited view of a model’s capacity to plan, act and adapt—capabilities that are increasingly central to applications such as autonomous tool use, code generation and cybersecurity. By tracking behavioural trajectories, ASAP reveals how agent‑relevant skills evolve during pretraining and how they diverge after post‑training. The paper’s results suggest the approach can serve as a practical diagnostic that complements conventional pretraining metrics, offering developers a clearer picture of a model’s interactive potential before costly fine‑tuning.
If the method gains traction, it could reshape how research labs and industry players assess LLMs, prompting earlier detection of strengths and weaknesses in agentic behavior. Watch for integration of ASAP‑style evaluations into upcoming model releases and benchmark suites, as well as follow‑up studies that test its predictive power across different architectures. The community’s response will indicate whether end‑to‑end agentic testing becomes a standard step in the LLM development pipeline.
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