TL;DR
Agent-World scales agent training by mining 1,978 real-world environments with 19,822 tools from the web. Using multi-environment reinforcement learning, Agent-World-8B achieves 61.8% on τ2-Bench and outperforms larger models across reasoning and coding benchmarks.
Agent-World scales agent training by mining 1,978 real-world environments with 19,822 tools from the web. Using multi-environment reinforcement learning, Agent-World-8B achieves 61.8% on τ2-Bench and outperforms larger models across reasoning and coding benchmarks.
21
已证实
17
证据不足
2
无法验证
N/A
可复现性
核心问题
How can we build scalable, realistic training environments for general-purpose agents and develop robust training methods for long-horizon, state-intensive tasks that require multi-tool orchestration and state tracking?
核心方法
{'approach': 'Agent-World unifies two components: (1) Agentic Environment-Task Discovery, which collects real-world themes, mines topic-aligned databases and executable tools from the web, and synthesizes verifiable tasks through graph-based and programmatic approaches; (2) Continuous Self-Evolving Agent Training, which uses multi-environment reinforcement learning (GRPO) with executable rewards and a diagnostic arena that identifies capability gaps to drive targeted environment expansion.', 'key_components': ['Agent-World unifies scalable environment-task discovery with continuous self-evolving agent training in a closed-loop system.', 'The Agentic Environment-Task Discovery component autonomously mines topic-aligned databases and executable tool interfaces from the web to form a realistic environment ecosystem.', 'The Continuous Self-Evolving Agent Training component uses multi-environment reinforcement learning with executable rewards for state-aware supervision.', 'The environment ecosystem serves as a dynamic diagnostic arena that identifies capability gaps and drives targeted environment-task expansion.', 'The two components enable co-evolution of agent policies and environments through iterative feedback.'], 'section_ids': ['sec_2']}
论点验证
已证实 (85%)
We propose Agent-World, a general-purpose agent training arena that unifies scalable environment-task discovery with continuous self-evolving agent training.
The paper fully specifies the Agent-World system with two tightly coupled components (Section 3.1 and 3.2) and provides extensive experimental validation across 23 benchmarks (Table 1, Figure 6). The system architecture is detailed, and its effective
The paper fully specifies the Agent-World system with two tightly coupled components (Section 3.1 and 3.2) and provides extensive experimental validation across 23 benchmarks (Table 1, Figure 6). The system architecture is detailed, and its effective
已证实 (85%)
Agentic Environment-Task Discovery: We collect thousands of real-world environment themes and build a deep-research pipeline that autonomously mines topic-aligned databases and executable tool interfaces from the web, forming a scalable and realistic environment ecosystem.
The paper provides concrete statistics: 1,978 environments retained after filtering, 19,822 distinct tools (p_44, Figure 4). The methodology for environment-theme collection (p_11-p_14), database mining (p_15-p_18), and tool generation (p_19-p_25) is
The paper provides concrete statistics: 1,978 environments retained after filtering, 19,822 distinct tools (p_44, Figure 4). The methodology for environment-theme collection (p_11-p_14), database mining (p_15-p_18), and tool generation (p_19-p_25) is
已证实 (85%)
Continuous Self-Evolving Agent Training: We train agents via multi-environment reinforcement learning over "agent-tool-database" interaction rollouts, using executable rewards for state-aware supervision.
The paper fully specifies the multi-environment RL training methodology (p_49-p_60) with executable rewards (p_56-p_58). Training results are demonstrated with GRPO curves (Figure 9) and benchmark improvements (Table 1).
The paper fully specifies the multi-environment RL training methodology (p_49-p_60) with executable rewards (p_56-p_58). Training results are demonstrated with GRPO curves (Figure 9) and benchmark improvements (Table 1).
证据不足 (60%)
We systematically gather environment themes from three real-world sources: (1) MCP Servers: We obtain real-world MCP server specifications from Smithery. (2) Tool Documentations: We broadly collect and filter open-source datasets covering real tool-use scenarios. (3) Industrial PRDs: As product requirement documents for specific industries, PRDs naturally include background, domain workflows and system interfaces.
The three sources are clearly specified (p_11-p_13), but the paper provides no justification for why these specific three sources were chosen, no comparison to alternative sources, and no ablation showing the contribution of each source. This is a de
The three sources are clearly specified (p_11-p_13), but the paper provides no justification for why these specific three sources were chosen, no comparison to alternative sources, and no ablation showing the contribution of each source. This is a de
证据不足 (55%)
We design an agentic workflow to autonomously mine and process web data into environment databases. Concretely, we build a deep-research agent G centered on a policy model πθ and an external toolset T including search, browser, code compiler, and operating-system (OS) tools.
The agentic workflow is specified (p_15-p_16) with policy model and toolset, but there's no justification for why this particular design was chosen over alternatives, no ablation comparing different toolsets, and no analysis of the workflow's effecti
The agentic workflow is specified (p_15-p_16) with policy model and toolset, but there's no justification for why this particular design was chosen over alternatives, no ablation comparing different toolsets, and no analysis of the workflow's effecti
证据不足 (50%)
We introduce a database complexification process φ, which iteratively prompts a deep-research agent to expand and enrich topic-specific databases. In practice, repeating this procedure for N rounds produces high-quality databases that better match realistic environment demands.
The complexification process is described (p_17-p_18), but there's no ablation comparing different N values, no analysis showing that N rounds produces better databases than alternatives, and no quantitative evidence that this process improves qualit
The complexification process is described (p_17-p_18), but there's no ablation comparing different N values, no analysis showing that N rounds produces better databases than alternatives, and no quantitative evidence that this process improves qualit
证据不足 (55%)
To construct a database-grounded executable toolset, we introduce a coding agent ψ equipped with a code compiler and OS tools. A tool is retained only if: the function can be successfully compiled by the Python compiler; Acc(f; Ĉf) > 0.5 on its associated test set; the corresponding environment contains at least one valid tool and one valid test case.
The filtering criteria are clearly specified (p_21-p_24), but the specific thresholds (Acc > 0.5, at least one valid tool/test case) are not justified through ablation or comparison. Why 0.5 and not 0.6 or 0.4? The paper doesn't provide evidence for
The filtering criteria are clearly specified (p_21-p_24), but the specific thresholds (Acc > 0.5, at least one valid tool/test case) are not justified through ablation or comparison. Why 0.5 and not 0.6 or 0.4? The paper doesn't provide evidence for
已证实 (90%)
The taxonomy contains 20 first-tier labels, 50 second-tier labels, and over 2K third-tier labels, providing a foundation for cross-environment task synthesis and stratified arena construction.
Concrete quantitative evidence is provided in p_27 and Figure 3: 20 first-tier labels, 50 second-tier labels, and over 2K third-tier labels. The taxonomy construction methodology is also described.
Concrete quantitative evidence is provided in p_27 and Figure 3: 20 first-tier labels, 50 second-tier labels, and over 2K third-tier labels. The taxonomy construction methodology is also described.
证据不足 (55%)
We use two complementary synthesis strategies: graph-based task synthesis for modeling sequential tool dependencies, and programmatic task synthesis for modeling complex, non-linear reasoning and control flow.
The two synthesis strategies are described (p_28-p_42), but there's no ablation comparing graph-based vs programmatic approaches, no analysis of their relative contributions, and no justification for why these two specifically are optimal. The ration
The two synthesis strategies are described (p_28-p_42), but there's no ablation comparing graph-based vs programmatic approaches, no analysis of their relative contributions, and no justification for why these two specifically are optimal. The ration
证据不足 (50%)
We adopt a reverse-engineering paradigm: we first synthesize a valid tool-call sequence and then generate the corresponding task description.
The reverse-engineering paradigm is described (p_29), but there's no comparison to forward-generation approaches, no ablation showing this is better than alternatives, and no analysis of its advantages. The design choice is stated but not empirically
The reverse-engineering paradigm is described (p_29), but there's no comparison to forward-generation approaches, no ablation showing this is better than alternatives, and no analysis of its advantages. The design choice is stated but not empirically
证据不足 (50%)
We define three types of edges in tool graphs: Strong dependency (wij = 3): The input of tool fj strictly relies on the output of tool fi. Weak dependency (wij = 2): The input of fj can be derived from fi's output, but can also be obtained via other means. Independent edge (wij = 1): Tools with no parameter-level dependencies.
The three edge types with weights (3, 2, 1) are specified (p_30-p_33), but there's no justification for these specific weight values. Why 3, 2, 1 and not 4, 2, 1 or 3, 2, 0? No ablation or sensitivity analysis is provided.
The three edge types with weights (3, 2, 1) are specified (p_30-p_33), but there's no justification for these specific weight values. Why 3, 2, 1 and not 4, 2, 1 or 3, 2, 0? No ablation or sensitivity analysis is provided.
证据不足 (55%)
We execute τ* step-by-step within a Python sandbox, recording the intermediate execution trace and the final return results. Observing the actual data fields and formats allows the LLM to refine qinit into a highly realistic and well-grounded final query qfinal.
The sandbox execution process is described (p_35), but there's no comparison to alternative refinement approaches, no ablation showing this improves task quality, and no quantitative analysis of the refinement's effectiveness.
The sandbox execution process is described (p_35), but there's no comparison to alternative refinement approaches, no ablation showing this improves task quality, and no quantitative analysis of the refinement's effectiveness.
证据不足 (50%)
To ensure task stability, we evaluate the generated task (qfinal, a*) by deploying a ReAct agent to solve it 5 separate times within the sandbox. We retain the task only if the agent successfully reaches a consistent answer in at least two independent runs.
The stability check (5 runs, ≥2 consistent) is specified (p_36), but there's no justification for these specific thresholds. Why 5 runs and not 3 or 10? Why ≥2 and not ≥3? No ablation or analysis is provided.
The stability check (5 runs, ≥2 consistent) is specified (p_36), but there's no justification for these specific thresholds. Why 5 runs and not 3 or 10? Why ≥2 and not ≥3? No ablation or analysis is provided.
证据不足 (55%)
To increase task difficulty while maintaining solvability, we complicate the reasoning path in each task. Specifically, we scale difficulty by increasing the maximum step count of the random walk to expand the tool chain, and by increasing the sampling probability of weak dependencies and independent edges to reduce reliance on obvious sequential outputs.
The difficulty scaling strategies are described (p_37), but there's no controlled ablation showing each strategy's individual contribution, no comparison of different scaling parameters, and the effectiveness is inferred from aggregate results rather
The difficulty scaling strategies are described (p_37), but there's no controlled ablation showing each strategy's individual contribution, no comparison of different scaling parameters, and the effectiveness is inferred from aggregate results rather
已证实 (90%)
Agent-World covers a broad range of environment types, with over 2,000 environments in total (1,978 retained after filtering). Each environment is equipped with a diverse toolset, averaging more than 10 tools, with some environments containing over 40 tools. The overall ecosystem includes 19,822 distinct tools.
Concrete quantitative statistics are provided in p_44 and Figure 4: over 2,000 environments (1,978 retained), average >10 tools per environment, some with >40 tools, 19,822 distinct tools total. These are specific, measurable claims with supporting d
Concrete quantitative statistics are provided in p_44 and Figure 4: over 2,000 environments (1,978 retained), average >10 tools per environment, some with >40 tools, 19,822 distinct tools total. These are specific, measurable claims with supporting d
已证实 (90%)
All synthesized tasks contain at least 7 interaction turns, with an average of over 20 turns and a non-trivial portion exceeding 40 turns, already indicating substantial difficulty.
Concrete quantitative evidence in p_45 and Figure 4(e): all tasks have at least 7 interaction turns, average over 20 turns, some exceeding 40 turns. Specific numbers are provided.
Concrete quantitative evidence in p_45 and Figure 4(e): all tasks have at least 7 interaction turns, average over 20 turns, some exceeding 40 turns. Specific numbers are provided.
已证实 (85%)
Only a small fraction of tasks are solved in all 10 attempts; most are solved only once out of 10, and some are not solved at all. This shows that our difficulty scaling strategy is effective at increasing task complexity.
Pass@10 evaluation results are reported in p_45 and Figure 4(f): only small fraction solved in all 10 attempts, most solved only once, some not solved at all. This provides quantitative evidence for difficulty scaling effectiveness.
Pass@10 evaluation results are reported in p_45 and Figure 4(f): only small fraction solved in all 10 attempts, most solved only once, some not solved at all. This provides quantitative evidence for difficulty scaling effectiveness.
证据不足 (55%)
We instantiate two reward types: (i) Graph-based tasks provide a structured rubric R = {rj}nj=1. We use a rubric-conditioned LLM-as-judge to evaluate each criterion rj from model output y under task x. (ii) Programmatic tasks provide an executable validation script Vcode per task, which we run in the sandbox to verify either the predicted answer or the resulting database state.
The two reward types are specified (p_56-p_58), but there's no ablation comparing different reward designs, no analysis of their relative effectiveness, and no justification for why these specific reward mechanisms were chosen.
The two reward types are specified (p_56-p_58), but there's no ablation comparing different reward designs, no analysis of their relative effectiveness, and no justification for why these specific reward mechanisms were chosen.
证据不足 (50%)
To enable stable training with environment interaction, we adopt Group Relative Policy Optimization (GRPO) to directly maximize the verifiable returns defined above.
GRPO is adopted (p_59) with reference to prior work [84], but there's no comparison to other RL algorithms (PPO, DPO, etc.), no ablation showing GRPO's advantages for this specific setting, and the choice is justified only by citation rather than emp
GRPO is adopted (p_59) with reference to prior work [84], but there's no comparison to other RL algorithms (PPO, DPO, etc.), no ablation showing GRPO's advantages for this specific setting, and the choice is justified only by citation rather than emp
证据不足 (50%)
For each first-tier category c ∈ C, we randomly select K environments (K = 5) and merge them into the arena set. This design ensures broad coverage over different environment types while keeping evaluation cost controllable.
K=5 environments per category is specified (p_62), but there's no justification for this specific value, no sensitivity analysis comparing different K values, and no analysis of coverage vs cost tradeoffs.
K=5 environments per category is specified (p_62), but there's no justification for this specific value, no sensitivity analysis comparing different K values, and no analysis of coverage vs cost tradeoffs.
... 共 40 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- No code available - core implementation of environment-task discovery pipeline and training framework not accessible
- No data available - the 'thousands of real-world environment themes', synthesized tasks, and training trajectories not released
- Environment-task discovery pipeline details missing - how environment themes are collected, how databases are mined, how tool interfaces are discovered, and how graph-based/programmatic task generation works are not specified
- Training hyperparameters incomplete - learning rate, number of training epochs/steps, optimizer, total training iterations not specified
- Cold-start SFT details missing - data format, SFT hyperparameters (learning rate, epochs, batch size), and exact generation procedure for 40K trajectories not provided
- Reward function details missing - 'executable rewards' mentioned but computation method not explained
- Hardware specifications not provided - GPU type, number of GPUs, training time not mentioned
- Random seeds not specified for reproducibility of experiments
- In-house evaluation framework not publicly available - exact evaluation protocols and subset selections for benchmarks like GAIA and HLE not detailed
- Model accessibility unclear - GPT-OSS-120B and Doubao-Seed-1.8 policy models may not be publicly available
局限性（作者自述）
- From 500 to 2000 environments, the trend remains upward but the marginal improvement gradually decreases, indicating diminishing-yet-positive returns at larger scales.
- Second-round gains are smaller than first-round gains but remain positive, reflecting diminishing yet still effective returns.
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分析时间：2026-04-22T13:37:15+00:00 · 数据来源：Paper Collector