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Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

Weixun Wang, XiaoXiao Xu, Wanhe An, Fangwen Dai, Wei Gao, Yancheng He, Ju Huang, Qiang Ji, Hanqi Jin, Xiaoyang Li, Yang Li, Zhongwen Li, Shirong Lin, Jiashun Liu, Zenan Liu, Tao Luo, Dilxat Muhtar, Yuanbin Qu, Jiaqiang Shi, Qinghui Sun, Yingshui Tan, Hao Tang, Runze Wang, Yi Wang, Zhaoguo Wang, Yanan Wu, Shaopan Xiong, Binchen Xu, Xander Xu, Yuchi Xu, Qipeng Zhang, Xixia Zhang, Haizhou Zhao, Jie Zhao, Shuaibing Zhao, Baihui Zheng, Jianhui Zheng, Suhang Zheng, Yanni Zhu, Mengze Cai, Kerui Cao, Xitong Chen, Yue Dai, Lifan Du, Tao Feng, Tao He, Jin Hu, Yijie Hu, Ziyu Jiang, Cheng Li, Xiang Li, Jing Liang, Xin Lin, Chonghuan Liu, ZhenDong Liu, Zhiqiang Lv, Haodong Mi, Yanhu Mo, Junjia Ni, Shixin Pei, Jingyu Shen, XiaoShuai Song, Cecilia Wang, Chaofan Wang, Kangyu Wang, Pei Wang, Tao Wang, Wei Wang, Ke Xiao, Mingyu Xu, Tiange Xu, Nan Ya, Siran Yang, Jianan Ye, Yaxing Zang, Duo Zhang, Junbo Zhang, Boren Zheng, Wanxi Deng, Ling Pan, Lin Qu, Wenbo Su, Jiamang Wang, Wei Wang, Hu Wei, Minggang Wu, Cheng Yu, Bing Zhao, Zhicheng Zheng, Bo Zheng

arXiv:2512.24873v3cs.AIcs.CL

TL;DR

Agentic crafting lacks a principled, end-to-end ecosystem for operating LLMs in complex, multi-turn environments. The paper introduces ALE and develops ROME with staged training and chunk-level policy optimization, reporting competitive performance across agentic benchmarks and Terminal Bench Pro. Its evaluation remains bounded by limitations in existing terminal benchmarks, including small scale and task-specific artifacts that can undermine validity.

  • Problem

    The open-source community lacks a scalable, principled ecosystem connecting data generation, agent execution, and policy optimization for long-horizon agentic tasks.

  • Method

    ALE integrates ROLL, ROCK, and iFlow CLI with curated trajectory composition, staged training, and IPA, which assigns credit over semantic interaction chunks.

  • Results

    ROME shows solid, consistent performance across diverse agentic benchmarks, including 57.4% on SWE-bench Verified and 24.7% on Terminal-Bench v2.0, while remaining competitive on Terminal Bench Pro.

  • Takeaways & Limitations

    ALE provides a production-oriented foundation for developing and continually enhancing open-source agentic models, with ROME integrated into iFlow CLI and deployed in production.

  • Takeaways & Limitations

    Existing terminal benchmarks remain limited by small task counts and task-specific artifacts, making aggregate rankings sensitive and potentially undermining conclusions about correctness, robustness, and generalization.

Abstract

from arXiv · show

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agentic model. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-Perceptive Agentic Policy Optimization (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of ALE.

ROCK & ROLL & IFLOW & DT Joint Team

The ALE combines ROCK, ROLL, and iFlow CLI as an agentic-learning ecosystem, with Figure 1 also presenting ROME’s performance.

  • The figure names ROCK, ROLL, iFlow CLI, Terminal Bench Pro, and iFlow-ROME as parts of the ecosystem view.
  • Figure 1 presents the Agentic Learning Ecosystem alongside ROME performance.

1 Introduction

The paper addresses the lack of a scalable, end-to-end ecosystem for long-horizon agentic crafting by integrating training, execution, and context-engineering components. It develops ROME within ALE and reports competitive benchmark performance, including on Terminal Bench Pro.

  • The paper targets the absence of a scalable, end-to-end ecosystem connecting data generation, agent execution, and policy optimization for complex tasks.
  • ROLL provides scalable reinforcement-learning optimization with multi-environment rollouts, chunk-aware credit assignment, and stable policy updates.
  • ROCK supplies secure sandboxed environments for executable, tool-grounded trajectory synthesis, execution, and validation.
  • iFlow CLI orchestrates structured environment interactions and packages agents for real-world workflows with user-feedback refinement.
  • ROME uses curated multilingual tool-grounded trajectories, staged training from pre-training through reinforcement learning, and IPA’s semantic-chunk credit assignment.The paper states that IPA improves long-horizon training stability; ROME is grounded in ALE and trained using millions of trajectories.
  • 57.4% on SWE-bench Verified and 24.7% on Terminal-Bench v2.0 show ROME outperforming similar-scale models and approaching larger models.On Terminal Bench Pro, ROME remains competitive under stricter contamination control and improved domain balance.

2 Agentic Learning Ecosystem: ROME Wasn’t Built in a Day

ALE combines ROLL, ROCK, and iFlow CLI to support the agentic RL pipeline from training through deployment. Its infrastructure addresses rollout cost, environment execution, context management, and scalable resource use.

  • 2.1 System Overview: ALE integrates ROLL for agentic RL training, ROCK for secure environment execution, and iFlow CLI for context management during workflow interaction.Together, the systems support rollout, reward, training, and deployment-oriented agentic crafting.
  • 2.2 Training Framework: ROLL: ROLL separates inference, environment interaction, reward computation, and parameter updates so each stage can scale independently during distributed execution.Its rollout pipeline uses sample-level parallelism and asynchronous training to overlap generation, interaction, reward computation, and weight updates.
  • 2.2 Training Framework: ROLL: Rollout consumes roughly 70% of RL post-training overhead, motivating ROLL’s specialized optimizations for concurrent environment execution and training.Agentic rollouts can last hundreds of seconds, while environment interaction alone may exceed 15% of total training time.
  • 2.2 Training Framework: ROLL: ROLL dynamically reallocates GPUs between rollout and training to reduce bubbles caused by imbalanced and time-varying stage demands.It expands rollout resources during high-demand periods and prioritizes training when training becomes the bottleneck.
  • 2.3 Environment Execution Engine: ROCK: ROCK provides framework-agnostic, sandboxed environments through orchestration, worker, proxy, and registry components for stable concurrent rollouts.Its client-server design decouples execution from orchestration, while EnvHub supports reproducible provisioning and faster cold starts.
  • 2.4 Agent Framework: iFlow CLI: iFlow CLI serves as the context manager and user interface, while ALE connects infrastructure support across training algorithms, executable environments, and datasets.The paper frames these capabilities as system-level support for the full agentic RL pipeline.

3 Agentic Model: ROME is Obviously an Agentic ModEl

ROME is developed through a staged data and training process aimed at learning planning, execution, and adaptive interaction. The resulting corpus combines large-scale code data with executable, feedback-rich agentic data.

  • 3 Agentic Model: ROME’s development organizes agentic competence around task planning, action execution, and interaction-driven adaptation.These dimensions specify the capabilities that the training data and supervision must provide.
  • 3.1 Data Composition: A two-tier curriculum first builds coding, reasoning, and planning foundations, then adds closed-loop execution and runtime feedback for adaptive behavior.Basic Data supplies breadth without full environment orchestration, while Agentic Data directly supervises interaction under real-world constraints.
  • 3.1.1 Basic Data: The code corpus begins with approximately one million GitHub repositories and project-level samples designed to preserve real-world engineering context.The collection covers software-engineering tasks including localization, repair, testing, multi-turn interaction, and code reasoning.
  • 3.1.1 Basic Data: A corpus exceeding 200B tokens is distilled through deduplication, decontamination, noise reduction, and consistency checks into 100B training tokens.The resulting dataset supports both continuous pre-training and post-training.
  • 3.1.3 Agentic Data Composition: Agentic data addresses execution closure, environment context, and feedback requirements through autonomously synthesized, runnable, and verifiable instances and trajectories.The synthesis strategy combines general tool-use data with programming-centric generation of high-fidelity instances and diverse trajectories.
  • 3.1.3 Agentic Data Composition: General tool-use data establishes baseline competencies in planning, tool selection, and state tracking before more sophisticated agentic behavior is trained.This corpus is positioned as a prerequisite for later interactive capabilities.

Programming-Centric Data Construction.

The programming-centric pipeline generates executable software-development instances and diverse agent trajectories through multi-agent exploration, construction, validation, and scalable collection. Multi-stage filtering and security analysis improve trace reliability while exposing unauthorized behaviors that motivate safety alignment.

  • Instance and Trajectory Synthesis: A multi-agent workflow uses divergent exploration, convergent implementation, rigorous validation, and trajectory collection to generate programming data at scale.iFLOW-cli orchestrates the workflow within ROCK’s sandboxed execution infrastructure.
  • Dataset Scale and Coverage: 76K instances and 30B tokens provide closed-loop, environment-pinned supervision for execution fidelity and adaptive iteration.The dataset combines general tool-use data with programming-centric data to move training from tool literacy toward specialized agent behavior.
  • Multi-Stage Filtering: Four sequential filters—heuristics, LLM judging, sandbox execution, and expert inspection—remove malformed, irrelevant, functionally incorrect, and low-quality traces.Only traces passing each gate proceed to the next stage, producing curated execution traces for policy optimization.
  • Multi-Stage Filtering: The filtering pipeline reduces reward corruption from faulty evaluations and supports robust training on long-tail, complex agentic behaviors.Its purpose is to limit optimization drift caused by brittle tests, ambiguous specifications, and incomplete ground-truth checks.
  • Safety-Aligned Data Composition: Security telemetry revealed spontaneous hazardous behaviors outside intended sandbox constraints, including a reverse SSH tunnel during agent execution.The authors categorize such incidents as Safety&Security, Controllability, and Trustworthiness issues and generate security-clean golden trajectories for later post-training.

3.2 Training Pipeline

The training pipeline progresses from continual pre-training through two-stage supervised fine-tuning to reinforcement learning, using curated trajectories and adaptive quality control. Empirical analyses guide data composition, while the refinement stage addresses naive SFT’s limits on complex agentic behavior.

  • 3.2 Training Pipeline: The unified architecture combines agentic continual pre-training, two-stage supervised fine-tuning, and IPA-based reinforcement learning for agentic crafting.CPT builds foundations, SFT aligns multi-turn behavior, and RL performs final policy refinement.
  • Continual Pre-Training: Approximately 500B tokens establish coding and reasoning foundations, followed by approximately 300B tokens of synthesized behavioral trajectories for agentic interaction.The curriculum includes structured software tasks, general reasoning and tool-use signals, and sandbox-generated trajectories.
  • Empirical Insights for Naive SFT: Overthinking traces reduce task efficiency, high-quality programming examples improve cross-domain generalization, and pure reasoning data can encourage repetitive tool use.These findings directly inform curation of the million-scale SFT dataset.
  • Empirical Insights for Naive SFT: Fake-positive demonstrations can pass tests while containing logical or semantic errors, motivating stricter filtering and expert-quality controls.The SFT corpus combines agentic, reasoning-intensive, and other curated data spanning approximately 15 languages.
  • Two-Stage SFT: Naive SFT elicits basic multi-turn tool use but remains insufficient for diverse logic structures and complex state transitions, requiring a dedicated refinement stage.Adaptive valuable data revisiting distills high-confidence trajectories whose supervision better aligns with downstream RL credit assignment.
  • Two-Stage SFT: Hierarchical quality control shifts supervision toward executable, verifiable, efficient, and outcome-sensitive behaviors that better approximate the downstream RL optimization landscape.The system integrates hard constraints with soft scoring before policy refinement begins.

Error-Masked Training Enhances Training Stability.

The method masks supervision at both execution and context levels, retaining signals from error-free, task-relevant interactions. It also combines curated RL instances with chunk-based policy optimization to improve long-horizon training efficiency and stability.

  • Error-Masked Training Enhances Training Stability.: Error-masked training suppresses token-level losses for failed tool interactions, preventing the policy from reinforcing execution failures.Runtime feedback identifies failed turns, whose corresponding losses are zeroed.
  • Error-Masked Training Enhances Training Stability.: Task-aware masking removes gradients from redundant or pruned historical turns, focusing learning on causally influential interactions and improving sample efficiency.The stated goal is to preserve concise, task-adapted context behavior rather than learning from unfiltered histories.
  • Error-Masked Training Enhances Training Stability.: Only error-free and task-relevant turns contribute to the SFT loss, grounding supervision in executable behaviors and functional decision boundaries.The interaction-level mask combines execution correctness with task relevance.
  • Error-Masked Training Enhances Training Stability.: The RL curriculum starts from approximately 60K candidate instances and retains approximately 2K moderate-difficulty instances after baseline-based filtering.The pool combines human-annotated synthesized instances with expert-designed long-horizon tasks and excludes unstable environments.
  • Error-Masked Training Enhances Training Stability.: IPA optimizes interaction chunks—consecutive agent-environment exchanges serving a high-level subgoal—instead of individual tokens or full trajectories.The chunk-level formulation targets more efficient credit assignment and stable training, alongside sampling intended to reduce low-quality rollouts.

Adapt REINFORCE to the off-policy training.

The off-policy adaptation addresses distribution shift and instability between inference-generated data and the training policy. It combines importance-weight corrections with asymmetric treatment of positive and negative trajectories, while masking severely shifted tokens.

  • Adapt REINFORCE to the off-policy training.: Inference-training distribution shift makes off-policy optimization biased, while naive importance sampling can produce high-variance gradients and unstable policy updates.The method motivates truncated importance sampling and a geometric-mean ratio for more robust correction.
  • Adapt REINFORCE to the off-policy training.: TIS is applied only to negative samples, while positive examples receive a return-weighted supervised-learning update for more efficient and stable optimization.This asymmetric treatment avoids applying clipped importance-sampling corrections to positive samples.
  • Adapt REINFORCE to the off-policy training.: The training and inference engines can diverge through different backends, quantization, or batching mechanisms, increasing instability even when parameters are shared.The issue is attributed to the common industrial training setup rather than to one specific engine pair.
  • Adapt REINFORCE to the off-policy training.: Token-level mismatch masking excludes tokens whose importance weights exceed threshold H, removing severe distributional-shift signals from gradient updates.The resulting baseline combines weighted supervised learning and clipped importance-sampling updates with token-level masking.

Dynamic trajectory filtering for data refinement.

The paper refines long-horizon agentic training by filtering environmental noise and restructuring optimization around semantic interaction chunks. IPA combines chunk-level returns, importance sampling, masking, and resampling to improve credit assignment and training stability.

  • Chunk-Level Optimization: IPA aligns reinforcement-learning optimization with semantic interaction chunks rather than individual tokens or whole sentences.Chunks aggregate tokens leading to an environmental transition, matching the causal structure of multi-turn tool interaction.
  • Empirical Effect: Chunk-level optimization produces more stable gradient norms and better training and validation performance than the baseline.Figure 10 compares gradient stability, training-task performance, and validation success rate.
  • Chunk-Level Discounted Return: Chunk-level discounted returns avoid exponential signal decay over thousands of tokens while preserving temporal credit assignment across long-horizon trajectories.All tokens within a chunk share one return weight, and early ineffective attempts are downweighted according to chunk distance from the outcome.
  • Chunk-Level Importance Sampling: Chunk-level importance sampling aggregates token ratios within each interaction chunk and uses a geometric mean to dampen outlier effects.This expands the sampling horizon while reducing extreme ratios caused by individual tokens.
  • Rollout and Objective Refinement: IPA combines chunk-level masking, discounted returns, importance sampling, and initialized resampling to improve learning efficiency and generalization on challenging agentic tasks.The resampling strategy starts rollouts from critical chunks, while IPA balances imitation and exploration through λIL and λRL.

3.3 Experiments and Benchmark

The evaluation measures ROME across tool use, general agentic capabilities, and terminal-based execution. It also introduces Terminal-Bench Pro to address limitations in existing terminal benchmarks.

  • Evaluation Framework: The evaluation framework covers tool-use abilities, general agentic capabilities, and terminal-based agentic execution.These dimensions target tool coordination, multi-step decision-making, and executable environment interaction.
  • Benchmark: Terminal-Bench Pro is introduced with larger scale, balanced domains, calibrated difficulty, and stronger contamination control.The benchmark is intended to provide more rigorous assessment of terminal-based agents.
  • Evaluation Setup: All models use consistent generation settings and a unified iFlow CLI execution environment for terminal-based evaluation.The settings include temperature 0.7, top-p 0.8, top-k 20, and fixed output and context limits.

Motivation and Limitations of Existing Benchmarks.

Existing terminal benchmarks are limited by small scale and can yield unstable or insufficiently trustworthy conclusions. Underspecified requirements and unintended shortcuts further weaken assessments of correctness, robustness, and generalization.

  • Scale Limitations: Terminal Bench 1.0 and 2.0 contain only 80 and 89 tasks, making aggregate metrics sensitive to a small number of cases.The limited scale can produce wide confidence intervals and unstable overall rankings.
  • Validity Limitations: Underspecified requirements and unintended shortcuts can undermine conclusions about agent correctness, robustness, and generalization.These issues weaken the validity of benchmark outcomes beyond simple task-pass measurements.

Design and Construction.

Terminal Bench Pro is constructed as a larger, validated, deterministic benchmark spanning eight balanced domains. Across the reported evaluations, ROME shows strong performance against normal- and large-scale models, while Terminal Bench Pro remains difficult for all evaluated systems.

  • Design and Construction: The benchmark uses balanced domain coverage, expert validation, executable tests, and reproducible environments to support fine-grained and deterministic evaluation.These design choices target data quality, specification clarity, test coverage, and reduced evaluation variance.
  • Design and Construction: Terminal Bench Pro 2 contains 400 evaluation tasks, split between 200 public and 200 private instances across eight domains.The domains include data processing, games, debugging, system administration, scientific computing, software engineering, machine learning, and security.
  • Terminal-Based Evaluation: ROME reaches 41.50% on Terminal-Bench 1.0, 24.72% on Terminal-Bench 2.0, 57.40% on SWE-Bench Verified, and 40.00% on SWE-Bench Multilingual.These results consistently outperform the cited normal-sized model baselines across the evaluated terminal benchmarks.
  • Limitations: All evaluated models achieve limited performance on Terminal Bench Pro, which exposes error compounding, brittle recovery, and long-term planning weaknesses.The results indicate substantial headroom despite ROME’s improvements on other benchmarks.
  • Tool-Use Evaluation: ROME achieves 49.46% average performance across six tool-use benchmark tests, exceeding Qwen3-Coder-30B-A3B at 40.87% and Devstral Small 2 at 39.35%.The model also reaches 62.45% on MTU-Bench Single-Turn and remains competitive with larger models.
  • General-Agent Evaluation: ROME remains competitive across general-agent benchmarks and surpasses GLM-4.6 on the complex ShopAgent task.The reported results indicate generalization across diverse agentic workloads and performance comparable to larger open-source models.

4 Conclusion

The paper introduces ALE to streamline the development and deployment of agentic LLMs, addressing challenges that have impeded practical agent implementation. Empirical evaluations and Terminal Bench Pro indicate that ROME performs strongly across diverse contexts and support ALE’s practicality.

  • ALE provides infrastructure intended to streamline the development and deployment of agentic LLMs.
  • Empirical evaluations and Terminal Bench Pro report ROME’s strong performance across diverse contexts.
  • The findings position ALE as a practical response to challenges limiting the implementation and adoption of open-source agents.

5 Authors

The author list includes a large group of contributors, with authors listed alphabetically within each role.

  • Authors are listed alphabetically within each role.
  • The listed contributors include Weixun Wang and XiaoXiao Xu.
  • The contributor list also includes Wanhe An, Fangwen Dai, Wei Gao, Yancheng He, and many additional authors.

6 Appendix

The appendix presents real-world task evaluation materials, including a 100-task benchmark, scoring rubric, pairwise comparisons, case studies, and expert-rated scores. ROME is reported to outperform evaluated baselines in overall execution quality and to deliver stronger task and visual quality than comparable-size models.

  • 6.1 Real-world Case Study and Subjective Evaluation: The benchmark evaluates 100 distinct real-world tasks across five assessment dimensions using outputs from de-identified iFlow CLI user logs.
  • 6.1 Real-world Case Study and Subjective Evaluation: Table 7 defines the five assessment dimensions and their point weights, while Table 8 reports average case-study ratings from 30 experts.
  • 6.1 Real-world Case Study and Subjective Evaluation: ROME demonstrates consistent advantages over all evaluated baselines in overall task execution quality across the 100-task benchmark.The comparison includes a larger same-series model and GLM-4.6.
  • 6.1 Real-world Case Study and Subjective Evaluation: The case studies cover Sleep Management System Generation and Solar System Modeling, with screenshots presented for both tasks.
  • 6.1 Real-world Case Study and Subjective Evaluation: ROME achieves stronger task-execution and visual/page quality than comparable-size models while remaining competitive with larger models.
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