Endless Terminals: Scaling RL Environments for Terminal Agents

Abstract: Environments are the bottleneck for self-improving agents. Current terminal benchmarks were built for evaluation, not training; reinforcement learning requires a scalable pipeline, not just a dataset. We introduce Endless Terminals, a fully autonomous pipeline that procedurally generates terminal-use tasks without human annotation. The pipeline has four stages: generating diverse task descriptions, building and validating containerized environments, producing completion tests, and filtering for solvability. From this pipeline we obtain 3255 tasks spanning file operations, log management, data processing, scripting, and database operations. We train agents using vanilla PPO with binary episode level rewards and a minimal interaction loop: no retrieval, multi-agent coordination, or specialized tools. Despite this simplicity, models trained on Endless Terminals show substantial gains: on our held-out dev set, Llama-3.2-3B improves from 4.0% to 18.2%, Qwen2.5-7B from 10.7% to 53.3%, and Qwen3-8B-openthinker-sft from 42.6% to 59.0%. These improvements transfer to human-curated benchmarks: models trained on Endless Terminals show substantial gains on held out human curated benchmarks: on TerminalBench 2.0, Llama-3.2-3B improves from 0.0% to 2.2%, Qwen2.5-7B from 2.2% to 3.4%, and Qwen3-8B-openthinker-sft from 1.1% to 6.7%, in each case outperforming alternative approaches including models with more complex agentic scaffolds. These results demonstrate that simple RL succeeds when environments scale.

• The paper presents an autonomous pipeline that procedurally generates verified RL environments for terminal agents, addressing data scarcity with a four-phase synthesis process.
• The methodology integrates task description generation, containerized validation, automated completion tests, and solution filtering to produce 3,255 diverse, solvable tasks.
• Empirical results demonstrate significant RL performance gains with improved pass rates on both in-domain and external benchmarks, despite challenges in complex domains.

Endless Terminals: Autonomous Procedural Generation for Scalable RL of Terminal Agents

Introduction

The paper "Endless Terminals: Scaling RL Environments for Terminal Agents" (2601.16443) presents an end-to-end procedural generation pipeline for constructing scalable, automatically verifiable RL environments tailored to the training of LLM-based agents in interactive terminal settings. The work addresses a critical bottleneck in the domain: the scarcity of diverse, human-verifiable environments suitable for training terminal agents with RL. Existing datasets are too limited to support RL at scale, and previous approaches have relied on distillation from proprietary models, risking ceiling effects, data leakage, and overfitting. This work proposes a fully autonomous approach, constructing a large corpus of solvable terminal-use tasks that enable consistent performance improvement through vanilla RL, without reliance on complex scaffolds or extrinsic data curation.

Figure 1: Endless Terminals generates tasks by four-stage procedural synthesis, culminating in a large set of RL-ready, verified environments.

Autonomous Pipeline for Procedural Task Generation

The Endless Terminals pipeline comprises four discrete phases, each autonomously executed and centered on generating challenging yet verifiable terminal-use tasks. The phases are as follows:

1. Task Description Generation: A LLM is prompted to synthesize rich, contextualized task specifications, sampling across domains (e.g., file management, scripting, database operations), complexity levels, and scenario contexts. Each task is defined by a user-facing instruction and a privileged ground-truth section containing exact system state and data.
2. Containerized Environment Setup and Validation: Candidate tasks are instantiated inside containerized environments (Docker/Apptainer). Automated prerequisite tests verify the starting state. The setup is iteratively refined until all prerequisites are satisfied or abandoned if unresolvable.
3. Completion Test Synthesis: For each task, automated scripts are generated to test for correct end states, based on privileged information. Strict validation ensures that completion tests fail in the initial state and only succeed on proper completion.
4. Solution-based Filtering: Each candidate task is filtered by sampling 16 solution attempts from a capable model (e.g., o3). Tasks are retained only if at least one solution is successful, eliminating under-specified or unsolvable instances.

   Figure 2: Each phase of the pipeline autonomously generates and validates tasks, containers, and tests, resulting in a comprehensive suite of non-trivial, solvable environments.

The pipeline yields a dataset with 3,255 verified, parameterized tasks. Task diversity is high both in structure and underlying competence required, with tasks distributed broadly across terminal-relevant categories.

RL Agent Design and Training Methodology

The agent framework is intentionally minimal. The base model interacts with the shell through a strictly turn-based loop:

• At each turn, the model receives full conversation history and shell output, then produces either a command (XML tagged) or signals completion.
• Shell state is maintained in persistent containers, allowing for non-trivial transformations of the environment across turns.
• Only basic instructions are provided, with no retrieval, code tools, or multi-agent scaffolding.
• Episodes end after a fixed turn/token budget or task completion.

RL is conducted purely with vanilla Proximal Policy Optimization (PPO) and a sparse, binary episode-level reward (1 if completion tests pass, 0 otherwise), with no stepwise rewards or explicit KL penalties.

Empirical Results

Diversity and Difficulty of Generated Tasks

The generated dataset spans heterogeneous task categories, with file operations and log management dominant but substantive representation across scripting, compression, DB operations, and more. Solution trajectories mostly require 1,000–4,000 characters, with a long tail up to 10,000+, indicating the environments support substantial multi-turn reasoning and long-term credit assignment.

Figure 4: Left—Distribution of solution lengths. Right—Distribution of pass rates by o3 model; tasks are neither trivial nor intractable.

Approximately half of tasks are solved by every o3 solution attempt, while the others present noteworthy variation in difficulty, spanning a realistic challenge space for RL policy improvement.

RL Performance Gains

Training agents with the procedural dataset and PPO consistently increases reward and pass rates on the development set, regardless of model capacity:

• Llama-3.2-3B: Dev set pass-rate improves from 4.0% → 18.2%
• Qwen2.5-7B: 10.7% → 53.3%
• Qwen3-8B-openthinker-sft: 42.6% → 59.0%

On external benchmarks, gains transfer despite distribution shift:

• TerminalBench 2.0: Largest model improves from 1.1% → 6.7%, outperforming stronger models trained with complex scaffolds or alternative RL approaches.

  Figure 3: PPO reward trajectories and pass rates during RL on in-domain dev, OpenThinker, and TerminalBench 2.0. RL on Endless Terminals yields consistent, transferable improvement over prevailing baselines and SFT.

Failure Analysis and Generalization

Performance on TerminalBench 2.0 remains limited by overall agent capacity (max pass@5 ≈ 23% in software engineering, 0% on mathematics/ML), with substantial drop-off on challenging or ambiguous tasks. Dominant failure modes include:

• Command loop behaviors (39%)
• Turn-budget exhaustion (26%)
• Domain knowledge gaps (specialized fields like cryptanalysis, bioinformatics)

Tasks solved successfully show much higher command diversity post-error, whereas failed tasks tend to repeat the same actions.

Figure 5: Performance varies significantly with task category and difficulty; agent capabilities peak for software-engineering, vanish in poorly-covered domains.

The pipeline’s solution filtering also imposes an implicit ceiling: all retained tasks are within the o3 model’s reach, so improvement beyond current frontier capabilities is unattainable in the generated distribution.

Implications and Future Directions

The results presented in this work have several notable implications for RL-based agent training and autonomous task generation:

• Scalable environment generation is decisive for RL progress in practical, multi-step agent domains. Algorithmic sophistication (e.g., complex scaffolds, partial rewards) yields diminishing returns without a high-throughput, diverse task stream for learning signals.
• Procedural pipelines can autonomously scale non-trivial environments with robust, automatic verification, removing the bottleneck of human curation and facilitating rapid benchmarking and iterative policy improvement.
• Limitations include the modeling of realistic user requests (ambiguity, implicit goals), the ceiling imposed by the filtering model, and lack of coverage in highly technical domains.

Promising future directions include:

• Conditional task modeling for more naturalistic and under-specified environments
• Dynamic self-play or curriculum construction to break the filtering capability ceiling and push agent boundaries
• Incorporation of richer agentic scaffolds, partial-reward shaping, and environment simulation to further boost sample efficiency
• Human-in-the-loop augmentation, selectively improving task quality and realism while maintaining scalability

Conclusion

Endless Terminals demonstrates that scalable, autonomous procedural generation of terminal-use RL environments enables measurable, transferable gains for LLM-based agents trained with simple RL. The approach overcomes key limitations of scale, diversity, and verification that have historically constrained progress in this domain. Nonetheless, gaps persist in modeling natural, ambiguous user tasks and scaling task difficulty beyond current model frontiers. Addressing these challenges will be essential for training robust, general-purpose agents capable of real-world terminal interactions and reasoning.