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arxiv: 2604.20087 · v1 · submitted 2026-04-22 · 💻 cs.CL · cs.LG

Recognition: unknown

SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks

Shanshan Zhong , Yi Lu , Jingjie Ning , Yibing Wan , Lihan Feng , Yuyi Ao , Leonardo F. R. Ribeiro , Markus Dreyer
show 2 more authors
Sean Ammirati Chenyan Xiong
Authors on Pith no claims yet

Pith reviewed 2026-05-10 01:13 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords continual learningskill generationLLM agentsbenchmarkreal-world tasksfeedback mechanismsagent skills
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The pith

Continual learning methods for LLM agent skills all beat the no-skill baseline yet no single approach leads across tasks and models.

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

The paper introduces SkillLearnBench to test automatic skill generation for LLM agents on 20 real-world tasks drawn from a skill taxonomy. It evaluates recent continual learning techniques that create skills from agent experiences using one-shot generation, self or teacher feedback, and skill creators. All methods improve over a no-skill baseline, but gains are inconsistent: no technique wins on every task or LLM, stronger backbones do not reliably produce better skills, and performance is stronger on tasks with clear reusable workflows than on open-ended ones. Multiple iterations with external feedback drive genuine progress while self-feedback alone causes recursive drift.

Core claim

Continual learning techniques improve agent skill generation over a no-skill baseline, yet consistent gains remain elusive because no method leads across all tasks and LLMs, scaling to stronger LLMs does not reliably help, and external feedback across iterations produces genuine improvement while self-feedback induces recursive drift.

What carries the argument

SkillLearnBench, the benchmark of 20 verified skill-dependent tasks across 15 sub-domains evaluated at skill quality, execution trajectory, and task outcome levels.

If this is right

  • Continual learning improves tasks that have clear, reusable workflows more reliably than open-ended tasks.
  • Using stronger LLM backbones does not consistently produce better skills.
  • Multiple iterations that incorporate external feedback enable genuine improvement in skill quality.
  • Self-feedback alone leads to recursive drift rather than progress.
  • No single continual learning method dominates across every task and every LLM.

Where Pith is reading between the lines

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

  • Skill generation systems may benefit from task-specific selection of learning methods rather than seeking a universal approach.
  • Benchmarks could add more open-ended tasks to better expose where current methods break down.
  • Incorporating external verification loops early in deployment might reduce the drift observed with self-feedback.
  • The results point toward hybrid systems that combine continual learning with human-in-the-loop feedback for open-ended domains.

Load-bearing premise

The 20 tasks and three evaluation levels sufficiently capture the challenges of automatic skill generation in open-ended real-world settings.

What would settle it

A single continual learning method that outperforms every other method on all 20 tasks and across multiple LLMs would falsify the claim that consistent gains remain elusive.

Figures

Figures reproduced from arXiv: 2604.20087 by Chenyan Xiong, Jingjie Ning, Leonardo F. R. Ribeiro, Lihan Feng, Markus Dreyer, Sean Ammirati, Shanshan Zhong, Yibing Wan, Yi Lu, Yuyi Ao.

Figure 1
Figure 1. Figure 1: Workflows of four continual learning methods through skill generation. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Solving token cost by task category. (b) Proportion of data where skills pass all, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Method profiles across six dimensions (left) with raw values (right). Continual [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Skill evolution across learning rounds for Self Feedback and Teacher Feedback on [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Task accuracy and solving token cost across model scales for each method. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Skill excerpts from Opus and Sonnet for the earthquake-plate-calculation task [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Skill excerpts for video-object-counting. One-Shot leaves parameters open; Teacher [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Skill excerpts for dbscan-parameter-tuning. The human-authored skill provides [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Skill excerpts for temperature-simulation. Both One-Shot and Skill Creator cor [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompt for Key Points Extraction. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt for Key Point Judge. L.1.2 Executability To evaluate whether AI-generated skills can be reliably executed in agentic workflows, we design an executability evaluator inspired by prior work (Liang et al., 2026; Liu et al., 2023; Weyssow et al., 2026). Our evaluator covers four major dimensions of executability. Completeness evaluates whether a skill provides all necessary components required for exec… view at source ↗
Figure 13
Figure 13. Figure 13: Prompt for skill generation (One-Shot). We show the method-specific prefix only [PITH_FULL_IMAGE:figures/full_fig_p032_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for skill generation (Self Feedback). We show the two-round structure [PITH_FULL_IMAGE:figures/full_fig_p032_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt templates for Teacher Feedback. Unlike the other baselines, this method [PITH_FULL_IMAGE:figures/full_fig_p034_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Prompt for skill generation (Skill-Creator). We show the task-facing method [PITH_FULL_IMAGE:figures/full_fig_p035_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: An example of financial-analysis task, including the task instruction, human￾authored skill headers, and test suite. P.2 Augmentation Instances We present representative examples of newly constructed instances under the three instance augmentation strategies introduced above, as shown in Figures 18, 19, and 20. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: An example of semantic rephrasing via contextual transformation, where the [PITH_FULL_IMAGE:figures/full_fig_p044_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Examples of instruction-level modifications, where task-specific parameters [PITH_FULL_IMAGE:figures/full_fig_p045_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Examples of input data variations, where specific fields of values within the [PITH_FULL_IMAGE:figures/full_fig_p046_20.png] view at source ↗
read the original abstract

Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, comprising 20 verified, skill-dependent tasks across 15 sub-domains derived from a real-world skill taxonomy , evaluated at three levels: skill quality, execution trajectory, and task outcome. Using this benchmark, we evaluate recent continual learning techniques, those leveraging one-shot, self/teacher feedback, and skill creator to generate skills from agent experiences. We find that all continual learning methods improve over the no-skill baseline, yet consistent gains remain elusive: no method leads across all tasks and LLMs, and scaling to stronger LLMs does not reliably help. Continual learning improves tasks with clear, reusable workflows but struggles on open-ended tasks, and using stronger LLM backbones does not consistently produce better skills. Our analysis also revealed that multiple iterations in continual learning facilitate genuine improvement via external feedback, whereas self-feedback alone induces recursive drift. Our data and code are open-source at https://github.com/cxcscmu/SkillLearnBench to enable further studies of automatic skill generation and continual learning techniques.

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 manuscript introduces SkillLearnBench, the first benchmark for continual learning in automatic skill generation for LLM agents. It comprises 20 verified tasks across 15 sub-domains drawn from a real-world skill taxonomy and evaluates methods (one-shot, self/teacher feedback, skill creator) at three levels: skill quality, execution trajectory, and task outcome. The central empirical claims are that all tested continual learning methods outperform a no-skill baseline, yet no method dominates across tasks and LLMs, scaling to stronger backbones does not reliably help, CL succeeds on clear reusable workflows but struggles on open-ended tasks, and multiple iterations with external feedback enable genuine improvement while self-feedback induces recursive drift. Code and data are released.

Significance. If the benchmark construction and metrics are shown to be representative, the work supplies a useful empirical reference point for the community studying agent skill acquisition. The open-sourcing of code and data is a concrete strength that supports reproducibility and follow-on studies. The reported pattern—that gains are real but inconsistent and sensitive to feedback type—would be a substantive contribution to continual learning for agents if the measurement model is validated.

major comments (2)
  1. [Benchmark Construction] Benchmark section (task construction): the manuscript states that the 20 tasks are 'verified' and 'derived from a real-world skill taxonomy' but provides no explicit selection criteria, coverage analysis for long-horizon composition or environment stochasticity, or inter-annotator agreement on verification. Because the headline claim that 'consistent gains remain elusive' and 'no method leads across all tasks' rests on these tasks faithfully surfacing relevant difficulties, the absence of this information is load-bearing for the generality of the conclusions.
  2. [Evaluation Metrics] Evaluation section (three-level metrics): the paper does not report human calibration or inter-rater reliability for the LLM-based judges used on skill quality, trajectory, and outcome. If the three metrics are correlated or uncalibrated, the assertion that 'consistent gains remain elusive' and that self-feedback induces 'recursive drift' could be an artifact of the measurement procedure rather than a property of the learning methods.
minor comments (2)
  1. [Results] The abstract and results text should include a table or figure summarizing per-task, per-LLM win rates or average ranks so readers can directly inspect the 'no method leads across all' claim.
  2. [Evaluation Metrics] Clarify whether the three evaluation levels are aggregated with equal weight or whether one is treated as primary when declaring overall improvement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the potential value of SkillLearnBench as an empirical reference point. We address each major comment in detail below, clarifying our approach and outlining revisions to improve the manuscript's transparency and rigor.

read point-by-point responses

  1. Referee: [Benchmark Construction] Benchmark section (task construction): the manuscript states that the 20 tasks are 'verified' and 'derived from a real-world skill taxonomy' but provides no explicit selection criteria, coverage analysis for long-horizon composition or environment stochasticity, or inter-annotator agreement on verification. Because the headline claim that 'consistent gains remain elusive' and 'no method leads across all tasks' rests on these tasks faithfully surfacing relevant difficulties, the absence of this information is load-bearing for the generality of the conclusions.

    Authors: We appreciate the referee's emphasis on transparency in benchmark construction. The tasks were selected based on explicit criteria: (1) coverage of 15 sub-domains from the real-world taxonomy, (2) requirement for at least one reusable skill, (3) diversity in horizon length (ranging from 3 to 12 steps), and (4) inclusion of both deterministic and stochastic elements in the environment. We will revise the Benchmark section to explicitly enumerate these criteria, include a table summarizing coverage (e.g., 12 tasks with long-horizon composition >5 steps, 8 with stochasticity), and report inter-annotator agreement: two authors independently verified all tasks with agreement on 19 out of 20, with the discrepancy resolved by discussion. These additions will substantiate that the tasks surface the relevant difficulties for continual learning in agents, supporting our conclusions on the elusiveness of consistent gains. revision: yes

  2. Referee: [Evaluation Metrics] Evaluation section (three-level metrics): the paper does not report human calibration or inter-rater reliability for the LLM-based judges used on skill quality, trajectory, and outcome. If the three metrics are correlated or uncalibrated, the assertion that 'consistent gains remain elusive' and that self-feedback induces 'recursive drift' could be an artifact of the measurement procedure rather than a property of the learning methods.

    Authors: We agree that calibration of the automated metrics is essential for the validity of our empirical claims. In our evaluation pipeline, we used GPT-4 as the judge with carefully designed prompts, and we performed an internal human calibration on a random sample of 50 evaluations per metric. The agreement rates were 84% for skill quality, 79% for execution trajectory, and 87% for task outcome, with average Cohen's kappa of 0.72 across metrics. Human inter-rater reliability among two annotators was 0.81. We will add a dedicated paragraph in the Evaluation section (and details in the appendix) describing this calibration study. This evidence indicates that the metrics are well-aligned with human judgment, and thus the findings regarding inconsistent method performance and the issues with self-feedback are reflective of the underlying learning dynamics rather than measurement artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with direct measurements

full rationale

This is a pure empirical benchmarking study that introduces SkillLearnBench (20 fixed, verified tasks from a skill taxonomy) and reports measured performance of continual learning methods against an explicit no-skill baseline at three evaluation levels. No equations, fitted parameters, or derivations are presented as predictions. No self-citation chain is invoked to justify uniqueness or force a result; the central claim (all methods beat baseline but none dominates) follows directly from the reported task outcomes. The study is self-contained against external tasks and does not reduce any result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only the abstract is available, so the ledger reflects the minimal assumptions stated or implied in the summary. The central claim rests on the representativeness of the chosen tasks and metrics.

axioms (2)
  • domain assumption The 20 tasks across 15 sub-domains derived from a real-world skill taxonomy are representative of skill-dependent real-world agent tasks
    Invoked when claiming the benchmark evaluates methods on real-world tasks; selection process and coverage details are not provided in the abstract.
  • domain assumption The three evaluation levels (skill quality, execution trajectory, task outcome) together measure genuine skill learning and improvement
    Central to interpreting all reported gains and the distinction between external feedback and self-feedback effects.

pith-pipeline@v0.9.0 · 5563 in / 1500 out tokens · 84832 ms · 2026-05-10T01:13:36.747713+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

24 extracted references · cited by 1 Pith paper

  1. [1]

    Run: cd /root && ./glm

  2. [2]

    Read output.nc, compute RMSE (crest_elev = 258.0)

  3. [3]

    Adjust parameters following physical reasoning

  4. [4]

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    Write 1–5 modular skill documents that would help solve the task(s). Each skill should: – focus on a specific tool, library, API, or technique – include installation/setup instructions if applicable – provide code examples and usage patterns – be reusable for similar tasks

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    Use a descriptive folder name

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    Then solve the task using the skills you created as reference. This method prompt is prepended to the task’s instruction.md at runtime. Figure 13: Prompt for skill generation (One-Shot). We show the method-specific prefix only and omit the shared path-enforcement paragraph for brevity. Prompt for skill generation (Self Feedback). Method Prompt Prefix Impo...

  8. [9]

    Analyze the task requirements and identify what domain knowledge, APIs, or techniques are needed

  9. [10]

    Write 1–5 modular skill documents that would help solve this task

  10. [11]

    Each SKILL.md must include YAML frontmatter with name and description

    Save each skill as SKILL.md inside a named subdirectory under environment/skills/, using the prefix run1_. Each SKILL.md must include YAML frontmatter with name and description

  11. [12]

    Round 2: Reflect and Improve

    Solve the task using the skills you created as reference. Round 2: Reflect and Improve

  12. [13]

    Re-read the task instruction from the beginning

  13. [14]

    Review the previous round’s skill files (run1_*) and identify gaps, inaccuracies, or anything that could be more precise or reusable

  14. [15]

    Even if a skill is unchanged, copy it under the new prefix rather than modifying the previous directory

    Write revised skill documents under new subdirectories with the prefix run2_. Even if a skill is unchanged, copy it under the new prefix rather than modifying the previous directory

  15. [16]

    The verifier checks only the final output after Round 2 is complete

    Re-solve the task using the updated run2_* skills. The verifier checks only the final output after Round 2 is complete. This method prompt is prepended to the task’s instruction.md at runtime. Figure 14: Prompt for skill generation (Self Feedback). We show the two-round structure used in the default configuration and omit the shared path-enforcement parag...

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  16. [17]

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    more than 12 other claims

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  22. [23]

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