Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement

Chengming Li; Dingwei Chen; Jie Jiang; Leo Luo; Peng Chen; Yang Li; Zefang Zong; Zhipeng Ma

arxiv: 2605.26952 · v1 · pith:KJWRHDAAnew · submitted 2026-05-26 · 💻 cs.CL

Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement

Dingwei Chen , Zefang Zong , Zhipeng Ma , Leo Luo , Yang Li , Chengming Li , Peng Chen , Jie Jiang This is my paper

Pith reviewed 2026-06-29 17:51 UTC · model grok-4.3

classification 💻 cs.CL

keywords agentic reinforcement learningknowledge boundarytool useLLM agentson-policy trainingquestion answeringreward shaping

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The pith

AKBE uses dual-path rollouts in agentic RL to identify each question's intrinsic knowledge boundary and reduce unnecessary tool calls.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets the tendency in agentic reinforcement learning for models to increase redundant tool calls and lose clarity on when their own parameters suffice. AKBE addresses this by running parallel with-tool and no-tool trajectories for each training instance and comparing their correctness to label whether tools are required and how many are minimal. These labels produce targeted signals that steer the policy toward efficient patterns inside the ongoing RL loop. On seven QA benchmarks the approach yields higher accuracy alongside fewer tool calls.

Core claim

AKBE defines the per-instance knowledge boundary as the determination of tool necessity and minimum calls needed, obtained by comparing correctness outcomes across dual-path rollouts. Trajectories are categorized from these comparisons and used to construct supervisory signals that guide efficient tool-use patterns, which are then inserted directly into the agentic RL training loop.

What carries the argument

Dual-path (with-tool and no-tool) rollouts whose correctness comparison categorizes instances and supplies per-question supervisory signals to the on-policy RL loop.

If this is right

Average task accuracy rises by 1.85 points over baseline agentic RL.
Tool calls fall by 18 percent, producing 25 percent higher tool productivity.
No accuracy-efficiency trade-off appears across the tested benchmarks.
The method integrates as a plug-and-play addition to multiple RL algorithms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same dual-path labeling could be tested on agent tasks outside question answering, such as web navigation or code execution.
Stabilizing the knowledge-boundary labels over many training epochs might further reduce variance in tool-use behavior.
If the boundary signals prove stable, they could serve as a lightweight calibration signal for deployed agents facing usage-cost constraints.

Load-bearing premise

That correctness differences between the with-tool and no-tool rollouts for each instance reliably mark the true intrinsic knowledge boundary and remain valid signals inside the continuing training loop.

What would settle it

A controlled experiment on the same seven QA benchmarks in which AKBE produces either lower average accuracy or no reduction in tool calls relative to standard agentic RL would falsify the reported gains.

Figures

Figures reproduced from arXiv: 2605.26952 by Chengming Li, Dingwei Chen, Jie Jiang, Leo Luo, Peng Chen, Yang Li, Zefang Zong, Zhipeng Ma.

**Figure 1.** Figure 1: Redundant tool-call growth during GRPO training (Qwen3-4B Multi-Hop). Samples correctly answered at early training (Step 20) with TC = 0/1/2 are tracked to late training (Step 240). Left: Tool calls increase substantially across all groups. Right: Trajectory degradation into original (still correct), redundant (correct but with extra TC), and hallucinated (degraded to incorrect due to noisy retrieval) c… view at source ↗

**Figure 2.** Figure 2: The framework of AKBE. For each question, dual-path rollouts (with-tool and no-tool) are performed in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Trajectory category distribution at early vs. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Effect of λ on Qwen3-4B Multi-Hop. AKBE improves over GRPO for λ ∈ [0.05, 0.2] (green region in (a)), with TC and TP consistently above GRPO across all values. 0 50 100 150 200 Training Step 0 2 4 6 8 10 12 14 Time per Step (min) 15% faster Training Time Comparison (Multi-Hop) GRPO (avg 8.5 min/step) AKBE (avg 7.2 min/step) [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Per-step training time comparison on Qwen3- [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: The prompt template for with-tool rollout in our experiment setting. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: The prompt template for no-tool rollout in our experiment setting. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Comparison of signal integration methods on Qwen3-4B Multi-Hop. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: Distribution of correct no-tool trajectory [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Per-step comparison of tool call counts ( [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

read the original abstract

Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs the model's intrinsic knowledge boundary, where the model fails to distinguish when tools are needed versus when parametric knowledge suffices. Existing solutions based on reward shaping create coarse-grained optimization targets that tend to incentivize indiscriminate tool-call suppression, leading to reward hacking. In this paper, we propose AKBE (Agentic Knowledge Boundary Enhancement), an on-policy method that dynamically probes the model's intrinsic knowledge boundary through dual-path (with-tool and no-tool) rollouts during training. We define the knowledge boundary as the per-instance determination of whether tools are required and the minimum tool calls necessary. By comparing correctness across paths, AKBE categorizes trajectories and constructs targeted supervisory signals that guide efficient tool-use patterns for each question. These signals are integrated seamlessly into the agentic RL training loop. Experiments on seven QA benchmarks demonstrate that AKBE improves task accuracy by +1.85 on average and reduces tool calls by 18% over standard agentic RL, yielding 25% higher tool productivity without any accuracy-efficiency trade-off. Further analysis suggests its plug-and-play compatibility across different RL algorithms and the mechanism of each signal category. Our code is available at https://github.com/CuSO4-Chen/AKBE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

AKBE's dual-path rollout labeling for per-instance tool needs is a concrete on-policy tweak that reports solid efficiency gains, but the lack of stability checks on those labels under policy updates is the main open question.

read the letter

The paper introduces AKBE as a way to keep agentic RL from drifting into unnecessary tool calls by probing each training instance with one with-tool and one no-tool rollout, then labeling whether tools are required and how many at minimum. Those labels become targeted signals inside the ongoing RL loop.

The dual-path comparison plus trajectory categorization looks like the actual new piece here. It is framed as an on-policy mechanism rather than a post-hoc reward hack, and the authors integrate it directly into standard agentic RL without changing the base algorithm. The reported results on seven QA benchmarks—an average +1.85 accuracy lift and 18% drop in tool calls, for 25% higher productivity—are the kind of numbers that matter for tool-using agents. Releasing the code is also useful.

The soft spot is exactly the one the stress-test flags. The method assumes the single-rollout comparison gives a stable, true boundary that stays valid once the policy starts updating. Because the model’s parametric knowledge shifts with each batch, early labels can become inconsistent or stale for later updates. The abstract gives no sign of mid-training re-probes, label-flip statistics, or ablations that would show how often the boundary moves. A single rollout per path also leaves room for rollout variance to mislabel the minimum call count. If the full paper does not address this, the efficiency claim rests on an untested assumption.

This is for people already running agentic RL on tool-augmented LLMs and looking for efficiency knobs. It is not a foundational shift, but the concrete mechanism and multi-benchmark numbers make it worth a referee’s time. I would send it out for review.

Referee Report

3 major / 2 minor

Summary. The paper proposes AKBE, an on-policy method for agentic RL that dynamically probes the model's intrinsic knowledge boundary via dual-path (with-tool and no-tool) rollouts per instance. Correctness comparisons define per-question supervisory signals for whether tools are required and the minimum calls needed; these signals are inserted into the ongoing RL loop to discourage redundant tool use. Experiments on seven QA benchmarks report +1.85 average accuracy, 18% fewer tool calls, and 25% higher tool productivity versus standard agentic RL, with no accuracy-efficiency trade-off and plug-and-play compatibility across RL algorithms.

Significance. If the results and underlying assumptions hold, the work offers a concrete mechanism to mitigate redundant tool calls in agentic RL without sacrificing task performance. The on-policy integration of boundary-derived signals and the public code release are strengths that could support follow-up work on efficient agent training.

major comments (3)

[Abstract] Abstract: the reported gains (+1.85 accuracy, 18% tool-call reduction) supply no statistical significance, standard deviations, baseline implementation details, or data-split information, preventing assessment of whether the improvements are reliable or reproducible.
[Method] Method (dual-path construction): the supervisory signals rest on the assumption that a single with-tool rollout realizes the minimal necessary calls and that correctness differences isolate intrinsic parametric knowledge rather than rollout variance or partial tool success; no multiple-rollout analysis or variance quantification is provided to support this load-bearing step.
[Experiments] Experiments / §4: no ablation re-probes knowledge-boundary labels mid-training or quantifies label stability under policy updates, despite the on-policy loop making earlier labels potentially stale; this directly affects whether the inserted signals remain valid targets.

minor comments (2)

[Abstract] Abstract: the metric 'tool productivity' is introduced without an explicit definition or formula.
[Experiments] The manuscript would benefit from a table listing per-benchmark results (accuracy and tool calls) rather than only averages.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point-by-point below and will revise the manuscript to incorporate clarifications and additional analyses where needed.

read point-by-point responses

Referee: [Abstract] Abstract: the reported gains (+1.85 accuracy, 18% tool-call reduction) supply no statistical significance, standard deviations, baseline implementation details, or data-split information, preventing assessment of whether the improvements are reliable or reproducible.

Authors: We agree that the abstract would benefit from greater statistical rigor and self-containment. In the revision we will add standard deviations from multiple random seeds, report statistical significance tests for the key metrics, and briefly note data splits and baseline implementation details directly in the abstract while retaining full details in §4. revision: yes
Referee: [Method] Method (dual-path construction): the supervisory signals rest on the assumption that a single with-tool rollout realizes the minimal necessary calls and that correctness differences isolate intrinsic parametric knowledge rather than rollout variance or partial tool success; no multiple-rollout analysis or variance quantification is provided to support this load-bearing step.

Authors: The single-rollout design per path is chosen to control compute cost during on-policy training. While we acknowledge that rollout variance could affect the boundary estimate, the correctness comparison is used only to generate coarse supervisory categories that are then refined by the ongoing RL objective. To strengthen this claim we will add a limited multi-rollout variance study on a data subset in the supplementary material. revision: partial
Referee: [Experiments] Experiments / §4: no ablation re-probes knowledge-boundary labels mid-training or quantifies label stability under policy updates, despite the on-policy loop making earlier labels potentially stale; this directly affects whether the inserted signals remain valid targets.

Authors: This is a legitimate concern about potential label staleness. We will add an ablation that re-probes boundary labels at multiple training checkpoints, measures label-change frequency, and reports downstream performance impact, placing the results in §4 or the appendix. revision: yes

Circularity Check

0 steps flagged

No circularity; experimental gains measured independently of boundary definitions

full rationale

The paper defines the knowledge boundary via dual-path rollouts and uses the resulting labels as supervisory signals inside the RL loop, then reports accuracy and tool-call reductions on seven external QA benchmarks. No equations, fitted parameters, or self-citations are shown that reduce the reported deltas to a re-labeling or re-use of the same inputs by construction. The central claims rest on empirical comparison against standard agentic RL rather than on any definitional equivalence or imported uniqueness result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that dual-path correctness comparison yields an accurate per-instance knowledge boundary; no free parameters or invented entities are described in the abstract.

axioms (1)

domain assumption Dual-path (with-tool and no-tool) rollouts can accurately determine whether tools are required and the minimum number of calls needed for each instance.
This premise is required to generate the supervisory signals that drive the efficiency gains.

pith-pipeline@v0.9.1-grok · 5797 in / 1298 out tokens · 28896 ms · 2026-06-29T17:51:19.677456+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

7 extracted references · 4 canonical work pages · 2 internal anchors

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work page internal anchor Pith review Pith/arXiv arXiv
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2022

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A 2 tgpo: Agentic turn-group policy optimiza- tion with adaptive turn-level clipping.arXiv preprint arXiv:2605.06200. Guanting Dong, Licheng Bao, Zhongyuan Wang, Kangzhi Zhao, Xiaoxi Li, Jiajie Jin, Jinghan Yang, Hangyu Mao, Fuzheng Zhang, Kun Gai, Guorui Zhou, Yutao Zhu, Ji-Rong Wen, and Zhicheng Dou

work page internal anchor Pith review Pith/arXiv arXiv

[2] [2]

Agentic entropy-balanced policy optimization

Agentic entropy-balanced policy optimization. Preprint, arXiv:2510.14545. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An

work page arXiv

[3] [3]

Group-in-Group Policy Optimization for LLM Agent Training

Group-in-group policy optimization for llm agent training.arXiv preprint arXiv:2505.10978. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. Constructing a multi- hop QA dataset for comprehensive evaluation of reasoning steps. InProceedings of the 28th Inter- national Conference on Computational Linguistics, pages 6609–6625, Barcelona...

work page internal anchor Pith review Pith/arXiv arXiv 2020

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Over-searching in search-augmented large lan- guage models.arXiv preprint arXiv:2601.05503. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Day- iheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. Qwen3 technical repor...

work page arXiv 2025

[5] [5]

is a large-scale Wikipedia-derived bench- mark with supporting-fact annotations, serving as a widely used testbed for multi-hop question answer- ing.2WikiMultiHopQA(Ho et al., 2020) com- bines Wikipedia passages with Wikidata triples, producing questions that require explicit multi-hop entity reasoning.MuSiQue(Trivedi et al., 2022) contains approximately ...

2020

[6] [6]

offers a small but adversarial set of compo- sitional queries, serving as a robustness probe for agentic RL policies. Single-Hop QA.This category verifies perfor- mance on single-step retrieval tasks.Natural Questions (NQ)(Kwiatkowski et al., 2019) aggre- gates real user queries answered from Wikipedia and serves as a standard benchmark for retrieval- aug...

2019

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at least one correct

features substantial lexical and syntactic di- vergence between questions and supporting ev- idence, testing robustness to surface variation. PopQA(Mallen et al., 2022) is an entity-centric benchmark designed to separate the contribution of external retrieval from parametric memoriza- tion, making it a natural diagnostic for whether the policy genuinely l...

2022