No Reset: Evaluating and Training Persistent Agents on Task Sequences

AI agents are typically evaluated one task at a time, yet deployed agents operate in persistent sessions where conversation history, memory, files, and prior experience carry across tasks. We study this mismatch by introducing sequential variants of GAIA2 and SWE-Bench Pro, in which agents solve sequences of up to 40 tasks within a single persistent session. Our experiments show that persistence has a model-dependent effect on task success. Opus 4.6 and GPT-5.4 remain near their independent baselines, whereas DeepSeek v4 Pro loses 5.9 percentage points on average. More consistently, persistence changes how every model behaves: agents use up to 60% fewer output tokens, repeat less exploratory work, and, on GAIA2, increase Python's share of tool calls by up to $4\times$. Trace analyses reveal both sides of this adaptation: agents reuse knowledge and customized tooling across tasks, but they can also under-explore new tasks and carry unchecked assumptions forward. To improve model capability in this setting, we introduce Sequential RL (Seq-RL), which trains on persistent multi-task rollouts with fine-grained credit assignment. Trained only on GAIA2-style data disjoint from the evaluation set, Seq-RL substantially narrows the sequential gaps on both GAIA2 splits. It also transfers zero-shot to SWE-Bench Pro, where it outperforms a single-task RL baseline trained on the same data, despite receiving no software-engineering data during RL. In short, sequential evaluation exposes new dimensions of agent reliability and in-context learning, while Seq-RL provides a promising training method for more robust long-lived agents.