SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment

Erpeng Xue; Hongxiang Lin; Lei Wang; Zhirui Kuai

arxiv: 2605.27899 · v1 · pith:HKPUOOW3new · submitted 2026-05-27 · 💻 cs.AI

SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment

Hongxiang Lin , Zhirui Kuai , Erpeng Xue , Lei Wang This is my paper

Pith reviewed 2026-06-29 13:09 UTC · model grok-4.3

classification 💻 cs.AI

keywords skill internalizationLLM agentscontrastive credit assignmentreinforcement learningautonomous performanceALFWorldWebShoppolicy optimization

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The pith

SkillC converts task-level contrasts between skill-injected and skill-free rollouts into a direct policy update signal for autonomous LLM agent performance.

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

The paper proposes SkillC to solve a limitation in skill-internalization RL for LLM agents. Prior internalization methods use skill contrasts only to control curriculum but leave the policy update unchanged, so they cannot credit autonomous success separately from skill-aided success. SkillC samples paired skill-injected and skill-free rollouts from the same policy and feeds their contrast into a dual-stream advantage estimator that preserves overall ranking while adding a one-sided push toward skill-free success. An adaptive curriculum then adjusts how strongly this contrast influences training. Experiments on ALFWorld and WebShop show the resulting agents outperform earlier internalization baselines while staying competitive with methods that keep external skills available at test time.

Core claim

SkillC samples paired skill-injected and skill-free rollouts for tasks from active skill types within the same policy update, and injects their task-level contrast into optimization via a dual-stream advantage estimator that preserves global ranking while applying a one-sided correction toward skill-free success. A smoothed validation-level signal further drives an adaptive curriculum over attribution strength, rollout allocation, and monotonic active-set pruning.

What carries the argument

Contrastive Skill Credit Assignment (CSCA) implemented through a dual-stream advantage estimator that applies a one-sided correction toward skill-free success while preserving global ranking.

If this is right

Without runtime skill access, SkillC surpasses the strongest prior skill-internalization RL baseline by 5.5% on ALFWorld and 4.4% on WebShop.
SkillC remains competitive with skill-augmented RL methods that retain external skills at inference time.
The dual-stream estimator distinguishes skill-dependent success from autonomous success during policy updates.
The adaptive curriculum uses validation signals to adjust attribution strength, rollout allocation, and active skill set size.
Monotonic active-set pruning progressively removes skills once their contrast signal weakens.

Where Pith is reading between the lines

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

The paired-rollout contrast mechanism could be applied in other RL settings where auxiliary information is available only during training.
If the estimator isolates skill effects cleanly, similar contrastive signals might reduce the need for hand-designed curricula across agent benchmarks.
Extending the approach to environments with noisier or partial skill prompts would test whether the one-sided correction remains effective.

Load-bearing premise

The task-level contrast between paired skill-injected and skill-free rollouts, when injected via the dual-stream advantage estimator, reliably distinguishes and promotes autonomous success rather than merely reflecting variance in rollout quality or policy stochasticity.

What would settle it

An ablation on ALFWorld or WebShop in which the dual-stream estimator is replaced by a standard advantage estimator that ignores the skill contrast, yet SkillC still shows the reported gains over prior internalization baselines.

Figures

Figures reproduced from arXiv: 2605.27899 by Erpeng Xue, Hongxiang Lin, Lei Wang, Zhirui Kuai.

**Figure 1.** Figure 1: Comparison of paradigms for skill use in Agentic RL. (a) Skill-augmented RL methods keep skills available at inference time and optimize performance with runtime skill support. (b) Skillinternalization RL methods withdraw skills during training and may estimate skill helpfulness for control, but leave task-level credit assignment unchanged. (c) SKILLC turns helpfulness comparison into contrastive credit … view at source ↗

**Figure 2.** Figure 2: Overview of SKILLC. Paired contrastive rollouts expose residual skill dependence for each task, a dual-stream advantage estimator redirects credit toward autonomous success without mixed-normalization bias, and a internalization-aware curriculum adapts attribution strength, rollout allocation, and active skill set. Stream 2 performs condition-wise normalization and reallocates credit according to the contr… view at source ↗

**Figure 3.** Figure 3: Internalization schedule during CSCA training on ALFWorld. (a) Internalization gate tracking the [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Training dynamics on ALFWorld. (a) With-skill and without-skill validation success rates over training [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Hyperparameter sensitivity on ALFWorld and each panel varies one CSCA hyperparameter. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Per-task training dynamics across six ALFWorld task categories. Each subplot shows the with-skill (solid [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

read the original abstract

Structured skill prompts improve exploration in long-horizon agentic reinforcement learning (RL). Skill-augmented RL methods retain external skills at inference, while skill-internalization RL methods withdraw them during training to enable autonomous performance. However, existing internalization approaches only use skill-helpfulness contrast for curriculum control, leaving the policy update unchanged and unable to distinguish skill-dependent from autonomous success. We propose SkillC, a framework based on Contrastive Skill Credit Assignment (CSCA) that converts this contrast into a direct learning signal for internalization. \textsc{SkillC} samples paired skill-injected and skill-free rollouts for tasks from active skill types within the same policy update, and injects their task-level contrast into optimization via a dual-stream advantage estimator that preserves global ranking while applying a one-sided correction toward skill-free success. A smoothed validation-level signal further drives an adaptive curriculum over attribution strength, rollout allocation, and monotonic active-set pruning. Experiments on ALFWorld and WebShop show that, without runtime skill access, SkillC surpasses the strongest prior skill-internalization RL baseline by 5.5\% and 4.4\%, respectively, while remaining competitive with skill-augmented RL methods.

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

SkillC turns paired rollout contrast into a direct policy update via dual-stream advantage, which prior internalization methods skipped, but the mechanism risks crediting stochastic wins in noisy long-horizon tasks.

read the letter

The core new piece is CSCA: it samples skill-injected and skill-free rollouts from the same policy, then feeds their task-level contrast into a dual-stream advantage estimator that keeps global ranking but applies one-sided correction toward skill-free success. Earlier internalization approaches used the contrast only for curriculum decisions and left the gradient unchanged. This is a clear difference in how the signal reaches the update.

The experiments report 5.5% and 4.4% gains over the strongest prior internalization baseline on ALFWorld and WebShop while staying competitive with skill-augmented methods. That is concrete evidence the change moves the needle on autonomous performance.

The main soft spot is the one the stress-test flags. In environments where success is rare and noisy, the estimator can reinforce lucky skill-free trajectories that would not repeat under more samples. The paper describes global ranking preservation and a smoothed validation signal, but nothing shown isolates true internalization from rollout variance. Without ablations that measure consistency across repeated rollouts or show the contrast correlates with later autonomous success rates, the gains could partly be artifacts.

The work is aimed at researchers building RL agents that need to drop external skills at inference. It deserves a serious referee because it identifies a real limitation in existing internalization methods and supplies a mechanism that can be tested and refined, even if the current evidence for why the mechanism succeeds is still limited.

Referee Report

2 major / 2 minor

Summary. The paper proposes SkillC, a framework for autonomous skill internalization in LLM agents via Contrastive Skill Credit Assignment (CSCA). It samples paired skill-injected and skill-free rollouts from the same policy, injects their task-level contrast into a dual-stream advantage estimator that preserves global ranking with one-sided correction toward skill-free success, and uses a smoothed validation signal for adaptive curriculum, attribution strength, rollout allocation, and active-set pruning. On ALFWorld and WebShop, SkillC outperforms the strongest prior internalization RL baseline by 5.5% and 4.4% without runtime skill access while remaining competitive with skill-augmented RL methods.

Significance. If the central performance claims hold under rigorous controls, the work supplies a direct learning signal for internalization that prior methods lacked, converting external skill contrast into policy updates rather than mere curriculum control. This could meaningfully advance autonomous long-horizon agents by reducing reliance on runtime skill prompts.

major comments (2)

[CSCA mechanism / dual-stream advantage estimator] The dual-stream advantage estimator (described in the abstract and the CSCA mechanism) applies a one-sided correction toward skill-free success while preserving global ranking. However, in low-success-rate, high-variance environments such as ALFWorld and WebShop, nothing in the stated construction (global ranking + one-sided correction + smoothed validation) prevents reinforcement of stochastic skill-free successes rather than internalized policy improvement. This directly affects whether the reported 5.5% and 4.4% gains can be attributed to credit assignment.
[Experiments on ALFWorld and WebShop] The experimental claims rest on single reported success rates without mention of multiple independent seeds, error bars, or ablation on rollout stochasticity (e.g., repeated sampling of skill-free trajectories per task). If the estimator credits lucky autonomous trajectories, the comparison to prior internalization baselines becomes unreliable.

minor comments (2)

Notation for the dual-stream advantage estimator and the smoothed validation signal should be formalized with explicit equations rather than prose description.
The abstract states that the method 'remains competitive with skill-augmented RL methods,' but the precise baselines and whether they use the same number of environment steps should be clarified in the experimental section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major comment below and indicate planned revisions where appropriate.

read point-by-point responses

Referee: [CSCA mechanism / dual-stream advantage estimator] The dual-stream advantage estimator (described in the abstract and the CSCA mechanism) applies a one-sided correction toward skill-free success while preserving global ranking. However, in low-success-rate, high-variance environments such as ALFWorld and WebShop, nothing in the stated construction (global ranking + one-sided correction + smoothed validation) prevents reinforcement of stochastic skill-free successes rather than internalized policy improvement. This directly affects whether the reported 5.5% and 4.4% gains can be attributed to credit assignment.

Authors: The paired rollout structure samples skill-injected and skill-free trajectories from the identical policy state, so the task-level contrast directly compares outcomes under matched conditions rather than independent stochastic draws. The dual-stream estimator preserves the global ranking across all trajectories while the one-sided correction only augments the advantage for skill-free successes that exceed their paired skill-injected counterpart; this prevents isolated lucky skill-free trajectories from receiving inflated credit unless they demonstrate superiority relative to the skill-injected baseline. The smoothed validation signal further modulates attribution strength and rollout allocation to dampen high-variance effects. We will add a clarifying subsection in the revised manuscript that formalizes this argument with a short derivation showing the bounded influence of stochastic outliers under the paired construction. revision: partial
Referee: [Experiments on ALFWorld and WebShop] The experimental claims rest on single reported success rates without mention of multiple independent seeds, error bars, or ablation on rollout stochasticity (e.g., repeated sampling of skill-free trajectories per task). If the estimator credits lucky autonomous trajectories, the comparison to prior internalization baselines becomes unreliable.

Authors: We agree that the current presentation reports aggregate success rates without explicit multi-seed statistics or stochasticity ablations. In the revised manuscript we will rerun the ALFWorld and WebShop evaluations across at least five independent random seeds, report means with standard deviations, and include an ablation that repeats skill-free trajectory sampling per task to quantify sensitivity to rollout variance. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation and evaluation are self-contained

full rationale

The provided abstract and context describe an empirical RL framework (CSCA with dual-stream advantage estimator, paired rollouts, and adaptive curriculum) evaluated on external benchmarks ALFWorld and WebShop. No equations, fitted parameters, or self-citations are shown that reduce any reported prediction or gain to a quantity defined by the method itself. The performance claims rest on external task success rates rather than internal redefinitions or tautologies, satisfying the default expectation of non-circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities can be extracted. The dual-stream advantage estimator and smoothed validation signal are presented as novel constructs but lack definitional detail here.

pith-pipeline@v0.9.1-grok · 5748 in / 1099 out tokens · 39878 ms · 2026-06-29T13:09:13.048868+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages · 5 internal anchors

[1]

Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

Group-in-group policy optimization for llm agent training.Advances in Neural Information Pro- cessing Systems, 38:46375–46408. Dawei Li, Zongxia Li, Hongyang Du, Xiyang Wu, Shi- hang Gui, Yongbei Kuang, and Lichao Sun. 2026a. Graph of skills: Dependency-aware structural re- trieval for massive agent skills.arXiv preprint arXiv:2604.05333. Hao Li, Chunjian...

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Skillrl: Evolving agents via recursive skill- augmented reinforcement learning.arXiv preprint arXiv:2602.08234. Renjun Xu and Yang Yan. 2026. Agent skills for large language models: Architecture, acquisition, security, and the path forward.arXiv preprint arXiv:2602.12430. An Yang, Baosong Yang, Beichen Zhang, and 1 others

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Qwen2.5 technical report.arXiv preprint arXiv:2412.15115. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. 2026. Self-distilled rlvr. arXiv preprint arXiv:2604.03128. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. 2022a. Webshop: Towards scalable real-world web intera...

work page internal anchor Pith review Pith/arXiv arXiv 2026
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Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Self-distilled reasoner: On-policy self- distillation for large language models.arXiv preprint arXiv:2601.18734. A Implementation Details Both ALFWorld and WebShop experiments use the same core CSCA hyperparameters, but differ in environment-specific configuration. ALFWorld.We train on 8×A100-80 GB GPUs with batch size 8 tasks/step, group size G=8, and le...

work page internal anchor Pith review Pith/arXiv arXiv

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Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

Group-in-group policy optimization for llm agent training.Advances in Neural Information Pro- cessing Systems, 38:46375–46408. Dawei Li, Zongxia Li, Hongyang Du, Xiyang Wu, Shi- hang Gui, Yongbei Kuang, and Lichao Sun. 2026a. Graph of skills: Dependency-aware structural re- trieval for massive agent skills.arXiv preprint arXiv:2604.05333. Hao Li, Chunjian...

work page internal anchor Pith review Pith/arXiv arXiv 2026

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EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

Evolver: Self-evolving llm agents through an experience-driven lifecycle.arXiv preprint arXiv:2510.16079. Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, and 1 others

work page internal anchor Pith review Pith/arXiv arXiv

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SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

Skillrl: Evolving agents via recursive skill- augmented reinforcement learning.arXiv preprint arXiv:2602.08234. Renjun Xu and Yang Yan. 2026. Agent skills for large language models: Architecture, acquisition, security, and the path forward.arXiv preprint arXiv:2602.12430. An Yang, Baosong Yang, Beichen Zhang, and 1 others

work page internal anchor Pith review Pith/arXiv arXiv 2026

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Qwen2.5 Technical Report

Qwen2.5 technical report.arXiv preprint arXiv:2412.15115. Chenxu Yang, Chuanyu Qin, Qingyi Si, Minghui Chen, Naibin Gu, Dingyu Yao, Zheng Lin, Weiping Wang, Jiaqi Wang, and Nan Duan. 2026. Self-distilled rlvr. arXiv preprint arXiv:2604.03128. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. 2022a. Webshop: Towards scalable real-world web intera...

work page internal anchor Pith review Pith/arXiv arXiv 2026

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Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Self-distilled reasoner: On-policy self- distillation for large language models.arXiv preprint arXiv:2601.18734. A Implementation Details Both ALFWorld and WebShop experiments use the same core CSCA hyperparameters, but differ in environment-specific configuration. ALFWorld.We train on 8×A100-80 GB GPUs with batch size 8 tasks/step, group size G=8, and le...

work page internal anchor Pith review Pith/arXiv arXiv