Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics
arXiv:2603.01209v1 Announce Type: new Abstract: Tool-augmented LLMs are increasingly deployed as agents that interleave natural-language reasoning with executable Python actions, as in CodeAct-style frameworks. In deployment, these agents rely on runtime state that persists across steps. By contrast, common training pipelines treat agent traces as token sequences, with execution semantics left implicit. This raises a data-centric question: Is state persistence merely an inference-time scaffold, or can models learn to exploit it when training data exposes the corresponding execution semantics? We isolate state persistence as a training-time variable. We introduce Opaque Knapsack, a procedurally generated family of partially observable optimization tasks designed to prevent one-shot solutions. Item attributes and constraints are hidden behind budgeted tool calls, forcing multi-turn control flow and iterative state revision. Holding task instances, prompts, tools, model, and supervisio
arXiv:2603.01209v1 Announce Type: new Abstract: Tool-augmented LLMs are increasingly deployed as agents that interleave natural-language reasoning with executable Python actions, as in CodeAct-style frameworks. In deployment, these agents rely on runtime state that persists across steps. By contrast, common training pipelines treat agent traces as token sequences, with execution semantics left implicit. This raises a data-centric question: Is state persistence merely an inference-time scaffold, or can models learn to exploit it when training data exposes the corresponding execution semantics? We isolate state persistence as a training-time variable. We introduce Opaque Knapsack, a procedurally generated family of partially observable optimization tasks designed to prevent one-shot solutions. Item attributes and constraints are hidden behind budgeted tool calls, forcing multi-turn control flow and iterative state revision. Holding task instances, prompts, tools, model, and supervision fixed, we generate paired trajectories differing only in whether interpreter state persists across steps or resets after each action. We then fine-tune identical base models (Qwen3-8B) on each trace variant and evaluate all four train-runtime combinations. Our 2x2 cross-evaluation shows that execution semantics primarily affect how agents reach solutions, not whether they do: solution quality is statistically indistinguishable across conditions, but token cost and stability differ substantially. A persistent-trained model in a stateless runtime triggers missing-variable errors in roughly 80% of episodes; a stateless-trained model in a persistent runtime redundantly re-derives retained state, using roughly 3.5x more tokens. Interpreter persistence should be treated as a first-class semantic of agent traces. Aligning fine-tuning data with deployment runtimes improves efficiency and reduces brittle train-runtime mismatches.
Executive Summary
This article presents a novel approach to training Large Language Models (LLMs) as agents that interleave natural-language reasoning with executable Python actions. The authors introduce Opaque Knapsack, a procedurally generated family of partially observable optimization tasks designed to prevent one-shot solutions. By isolating state persistence as a training-time variable, they demonstrate that execution semantics affect how agents reach solutions, rather than whether they do. The study suggests that interpreter persistence should be treated as a first-class semantic of agent traces and that aligning fine-tuning data with deployment runtimes improves efficiency and reduces brittle train-runtime mismatches. This research has significant implications for the development of more efficient and effective LLM-based agents.
Key Points
- ▸ The authors introduce Opaque Knapsack, a procedurally generated family of partially observable optimization tasks.
- ▸ State persistence is isolated as a training-time variable and evaluated in a 2x2 cross-evaluation.
- ▸ Execution semantics primarily affect how agents reach solutions, not whether they do.
Merits
Strength in Design
The authors' use of procedurally generated tasks and paired trajectory generation allows for a systematic evaluation of the impact of interpreter persistence on LLM-based agents.
Insightful Analysis
The study provides a thorough analysis of the effects of execution semantics on agent behavior and highlights the importance of aligning training data with deployment runtimes.
Demerits
Limited Generalizability
The study focuses on a specific implementation of LLM-based agents and may not be generalizable to other architectures or domains.
Need for Further Exploration
The article primarily focuses on the effects of interpreter persistence and does not explore other potential training-time variables that may impact agent behavior.
Expert Commentary
This article presents a significant contribution to the field of LLM-based agents, highlighting the importance of interpreter persistence and the need for aligning fine-tuning data with deployment runtimes. The authors' use of procedurally generated tasks and paired trajectory generation provides a systematic evaluation of the impact of interpreter persistence on agent behavior. While the study has limitations in terms of generalizability and exploration of other training-time variables, it provides a valuable insight into the effects of execution semantics on agent behavior. As the field of AI continues to evolve, research in this area will become increasingly important for developing more efficient and effective LLM-based agents.
Recommendations
- ✓ Recommendation 1: Future research should explore the impact of other training-time variables on agent behavior and develop more efficient and effective training methods.
- ✓ Recommendation 2: Developers should consider aligning fine-tuning data with deployment runtimes to improve the efficiency and effectiveness of LLM-based agents.