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arxiv: 2606.00135 · v1 · pith:R6UD3Q4Inew · submitted 2026-05-28 · 💻 cs.LG · cs.AI

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

Tong Liu , Cheng Qian , Matej Cief , Yuan He , Daniele Dan , Nikolaos Aletras , Gabriella Kazai This is my paper

Pith reviewed 2026-06-29 08:36 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords tool-callingLLM agentsreinforcement learningevaluation sensitivitymulti-turn interactionsRL efficiencypolicy optimizationrollout waste
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The pith

Tool-calling evaluations for LLM agents shift with small undocumented choices in seeds, prompts, and history handling, while RL training wastes compute on rollouts and updates that carry no signal.

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

The paper studies tool-calling in LLM agents along two axes. On effectiveness, it shows that reported performance changes substantially when random seeds, system prompts, multi-turn templates, or history carry-forward are altered, especially in multi-turn cases. On efficiency, it identifies waste from prompts that produce no learning signal during rollouts and from high-cost policy updates, then introduces two techniques that remove much of this waste. If these observations hold, current leaderboards cannot be compared reliably and standard RL pipelines can be made faster without loss of final performance.

Core claim

Tool-calling evaluation pipelines produce results that vary with minor, often undocumented choices including the random seed, system prompt, multi-turn template construction, and how prior interaction or reasoning history is carried forward; these variations are large enough in multi-turn settings to make leaderboard rankings unreliable. Standard RL for tool-calling incurs two forms of waste: many rollout prompts yield no learning signal and policy updates carry high computational cost. Two techniques that target these wastes deliver substantial wall-clock speedup while preserving performance and generalization.

What carries the argument

Sensitivity analysis of evaluation pipelines together with two techniques that eliminate no-signal rollouts and reduce the cost of policy updates in RL training.

If this is right

  • Leaderboard comparisons for multi-turn tool-calling are unreliable unless evaluation pipelines are standardized.
  • RL training for tool-calling agents can reach the same performance level in substantially less wall-clock time.
  • Results must document random seeds, prompts, templates, and history handling to be reproducible.
  • The acceleration techniques preserve generalization across different tool-calling settings.

Where Pith is reading between the lines

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

  • Similar sensitivity to implementation details is likely present in other multi-turn agent benchmarks.
  • The waste-reduction approach could shorten RL training in other domains that also produce many uninformative rollouts.
  • Community adoption of fixed test suites or reference implementations would be needed to make rankings stable.

Load-bearing premise

The performance differences arise primarily from the listed implementation choices and that the two identified wastes are the main bottlenecks that the new techniques can remove without harming learning quality.

What would settle it

Re-run a set of published tool-calling evaluations across multiple random seeds and check whether the ranking of methods changes; separately, train the same agent with and without the two proposed techniques and measure both wall-clock time and final task performance.

Figures

Figures reproduced from arXiv: 2606.00135 by Cheng Qian, Daniele Dan, Gabriella Kazai, Matej Cief, Nikolaos Aletras, Tong Liu, Yuan He.

Figure 1
Figure 1. Figure 1: Tool-calling performance across ten different random seeds on BFCL. Multiturn Template (Native) <SYS> SYSTEM_PROMPT </SYS> <USR> USER_QUERY 1 </USR> <AST> <think> THINKING PROCESS 1 </think> TOOL-CALLING CONTENT </AST> <USR> <tool_response> TOOL-CALLING RESPONSE </tool_response> <tool_response> TOOL-CALLING RESPONSE </tool_response> </USR> <AST> ASSISTANT RESPONSE </AST> <USR> USER QUERY 2 </USR> <AST> Mul… view at source ↗
Figure 2
Figure 2. Figure 2: Left: Native template. Middle: Context template. Right: Template without thinking history. We use abstract role markers (e.g., <SYS>, <USR>, <AST>) to represent model-specific chat-template tokens such as <|im start|>system in Qwen-series models and <|start header id|>system in Llama. risk for tool-calling: evaluation results may be misleading when models are evaluated using different system prompts, while… view at source ↗
Figure 3
Figure 3. Figure 3: (Left): Influence of multi-turn templates on tool-calling performance for two Qwen models on BFCL multi-turn category. (Middle): Influence of retaining thinking history on tool-calling performance for two Qwen models on BFCL multi-turn category. (Right): Influence of system prompt on BFCL multi-turn category [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Ratio of zero-variance vs. non-zero-variance prompts during RL training of Qwen2.5-3B-Instruct on the single-turn tool￾calling dataset and Qwen3-4B on the multi-turn data. Blue: ratio of prompts whose rollout rewards exhibit variance (i.e., useful learning signal). Orange: ratio of prompts with all rollouts achiev￾ing the maximum reward. In both cases, only around 20% prompts are effective prompts, indicat… view at source ↗
Figure 5
Figure 5. Figure 5: Temporal stability of all-correct prompts across train￾ing epochs. We plot P(still all-correct| (conti-k) all-correct), the probability that a prompt remains to have all-correct rollouts given its rollouts were all-correct for the previous k consecutive epochs. Both k = 1 and 2 exhibit high retention rates, demonstrating that recently-solved prompts exhibit strong temporal coherence and can be safely filte… view at source ↗
Figure 8
Figure 8. Figure 8: Entropy dynamics during tool-calling RL training. From left to right: total response token entropy, thinking-token entropy, and tool-calling token entropy, plotted as a function of training steps. Comparison to other models. We report a broader com￾parison against other representative open-source and closed￾source models in [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of vanilla GRPO and GRPO with efficiency methods for single-turn (left) and multi-turn (right) setting. Given the same or less wall-clock time, our method yields strictly better performance, demonstrating more effective utilization of rollout and policy update computation. extremes: S ∗ = {1, . . . , m′ } ∪ {n − (m − m′ ) + 1, . . . , n}, (7) i.e., m′ lowest-reward and m − m′ highest-reward roll… view at source ↗
Figure 9
Figure 9. Figure 9: Default BFCL evaluation system prompt. vs. pure multi-turn data. We construct two matched train￾ing datasets derived from xLAM (Zhang et al., 2025a) and ToolACE (Liu et al., 2024), following the preprocessing method in Zhang et al. (2025c). (i) Multi-turn set: we use the released multi-turn trajectories directly. (ii) Single-turn set: to avoid overly trivial instances, we filter examples by sampling 8 roll… view at source ↗
Figure 10
Figure 10. Figure 10: A stonger BFCL evaluation system prompt by slightly manual modification [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ratio of zero-variance vs. non-zero-variance prompts during RL training of Llama3.1-8B-Instruct and Qwen2.5-7B￾Instruct on the single-turn tool-calling dataset on the first 20 steps. The batch size here is half of that in [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
read the original abstract

Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This paper studies tool-calling along two complementary axes: effectiveness, i.e., how this capability is measured, and efficiency, i.e., how it is learned. On effectiveness, we systematically analyze tool-calling evaluation pipelines and show that results can be highly sensitive to seemingly minor, often undocumented implementation choices including the random seed, system prompt, multi-turn template construction, and how prior interaction/reasoning history is carried forward. These choices can lead to substantial differences in reported performance, especially in multi-turn settings where without rigorous standardization, leaderboard rankings are unreliable. On efficiency, we examine standard reinforcement learning (RL) for tool-calling and identify two sources of computational waste: (i) during rollouts, many prompts produce no learning signal, and (ii) during policy updates, optimization incurs high computational cost. Guided by these findings, we introduce two techniques that accelerate RL-based tool-calling training, achieving substantial wall-clock speedup without degrading performance.

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.

Referee Report

2 major / 2 minor

Summary. The paper claims that tool-calling evaluation pipelines for LLM agents are highly sensitive to minor, often undocumented choices (random seed, system prompt, multi-turn template construction, and history handling), leading to unreliable leaderboard rankings especially in multi-turn settings. It further identifies two sources of computational waste in standard RL for tool-calling (no-learning-signal rollouts and high-cost policy updates) and introduces two techniques that deliver substantial wall-clock speedup without degrading performance.

Significance. If substantiated, the effectiveness analysis would be a useful contribution by documenting sources of variance that undermine current agent benchmarks and calling for standardization. The efficiency techniques address practical bottlenecks in RL training and could be impactful for scaling tool-calling agents if the no-degradation result is shown to be robust. The paper's empirical approach to identifying waste sources is a strength, though the joint claims require careful cross-validation between the two axes.

major comments (2)
  1. [Effectiveness and efficiency sections] Effectiveness section: the demonstrated sensitivity to seed, prompt, template, and history handling directly bears on the efficiency claims. The central assertion that the two techniques achieve speedup 'without degrading performance' (and preserve learning quality) is load-bearing, yet the manuscript does not state whether the RL experiments employed the standardized pipelines advocated in the effectiveness analysis.
  2. [Efficiency experiments] RL experiments (efficiency portion): without explicit confirmation that evaluations used fixed seeds, consistent multi-turn templates, and documented history handling, any reported performance equivalence could be an artifact of a favorable configuration rather than a property of the proposed techniques. This makes the 'preserving learning quality' assumption a load-bearing point for the joint contribution.
minor comments (2)
  1. Add a dedicated reproducibility subsection detailing exact prompt templates, seed values, and history concatenation rules used in all reported experiments.
  2. Clarify the precise algorithmic definitions and hyperparameters of the two proposed techniques (e.g., how no-learning-signal detection is implemented and how policy-update cost is reduced).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments, which correctly identify an important linkage between the effectiveness and efficiency sections. We address each major comment below.

read point-by-point responses

  1. Referee: [Effectiveness and efficiency sections] Effectiveness section: the demonstrated sensitivity to seed, prompt, template, and history handling directly bears on the efficiency claims. The central assertion that the two techniques achieve speedup 'without degrading performance' (and preserve learning quality) is load-bearing, yet the manuscript does not state whether the RL experiments employed the standardized pipelines advocated in the effectiveness analysis.

    Authors: We agree that the sensitivity results have direct implications for interpreting the efficiency claims. The RL experiments were conducted using fixed random seeds, consistent multi-turn templates, and the documented history-handling protocol from the effectiveness analysis. This alignment was not explicitly stated in the original manuscript. In the revision we will add a dedicated paragraph in Section 4 (Efficiency Experiments) that documents the precise evaluation pipeline employed for all RL runs, including seed values, system prompt, template construction, and history handling, so that readers can verify the 'no degradation' result was obtained under the advocated standardized conditions. revision: yes

  2. Referee: [Efficiency experiments] RL experiments (efficiency portion): without explicit confirmation that evaluations used fixed seeds, consistent multi-turn templates, and documented history handling, any reported performance equivalence could be an artifact of a favorable configuration rather than a property of the proposed techniques. This makes the 'preserving learning quality' assumption a load-bearing point for the joint contribution.

    Authors: We concur that the absence of explicit confirmation leaves the performance-equivalence claim vulnerable to the interpretation raised. As noted in the response to the first comment, the experiments did follow the standardized pipeline. The revision will include the requested confirmation together with a short table or bullet list summarizing the fixed settings, thereby strengthening the joint contribution by making the evaluation protocol transparent and reproducible. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on direct experimental observation without self-referential derivations

full rationale

The paper's central contributions are an empirical sensitivity analysis of tool-calling evaluation pipelines (varying seeds, prompts, history handling) and experimental identification of RL waste sources followed by proposed acceleration techniques. No equations, fitted parameters renamed as predictions, or derivation chains appear. Claims are grounded in reported experimental outcomes rather than any reduction to inputs by construction or load-bearing self-citations. The analysis is self-contained against external benchmarks via direct measurement.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no details on any free parameters, axioms, or invented entities beyond standard concepts in RL and LLM agents.

pith-pipeline@v0.9.1-grok · 5734 in / 1310 out tokens · 45792 ms · 2026-06-29T08:36:56.936246+00:00 · methodology

discussion (0)

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Reference graph

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