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arxiv: 2502.03752 · v5 · pith:SQBGSVP3new · submitted 2025-02-06 · 💻 cs.LG · cs.AI

Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning

Sanghyeon Lee , Sangjun Bae , Yisak Park , Seungyul Han This is my paper

Pith reviewed 2026-05-23 03:19 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords meta-reinforcement learningskill-based RLnoisy demonstrationshierarchical policiesself-improving learninglong-horizon tasksoffline reinforcement learning
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The pith

SISL refines skills from noisy offline data via decoupled policies and return relabeling for stable meta-RL adaptation.

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

The paper proposes Self-Improving Skill Learning (SISL) to make skill-based meta-reinforcement learning reliable when offline demonstrations contain noise. It decouples high-level decision making from skill improvement policies and applies maximum return relabeling to prioritize useful trajectories during updates. This combination produces more stable skill learning and stronger performance on long-horizon tasks than prior methods. A sympathetic reader would care because imperfect demonstration data is common in practice, and current skill-based approaches degrade quickly under such conditions.

Core claim

SISL performs self-guided skill refinement using decoupled high-level and skill improvement policies, while applying skill prioritization via maximum return relabeling to focus updates on task-relevant trajectories, resulting in robust and stable adaptation even under noisy and suboptimal data.

What carries the argument

Decoupled high-level and skill-improvement policies combined with maximum-return relabeling to identify and refine task-relevant skills from noisy demonstrations.

If this is right

  • Reliable skill learning occurs from noisy offline data without requiring ground-truth labels.
  • Consistent outperformance on diverse long-horizon tasks compared with other skill-based meta-RL methods.
  • Stable adaptation to unseen tasks holds even when demonstrations are suboptimal.
  • Noise effects are mitigated by focusing updates on trajectories with highest returns.

Where Pith is reading between the lines

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

  • The return-relabeling step could be tested as a plug-in module for other hierarchical RL algorithms facing noisy data.
  • Evaluating SISL on physical robot platforms would show whether the learned skills transfer under real sensor noise.
  • Extending the same self-improvement loop to non-meta settings might improve robustness in standard offline RL.

Load-bearing premise

That the combination of decoupled high-level and skill-improvement policies plus maximum-return relabeling can reliably identify and refine task-relevant skills from noisy offline demonstrations without ground-truth labels or additional clean data.

What would settle it

An experiment that adds increasing levels of action or state noise to the offline dataset and measures whether SISL still outperforms standard skill-based meta-RL baselines at every noise level.

Figures

Figures reproduced from arXiv: 2502.03752 by Sanghyeon Lee, Sangjun Bae, Seungyul Han, Yisak Park.

Figure 1
Figure 1. Figure 1: Sample trajectories in the Maze2D environment: (a) Noisy demonstrations from the offline [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of prior skill learning methods in microwave-opening task: (a) Learned skills [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The SISL framework ℒ𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬𝐬 Maximum Return Relabeling 𝑮𝑮 � 𝝉𝝉 � 𝟏𝟏 Task 1 Task 2 Task 3 4.3 2.4 1.2 Relabeled Return 𝑝𝑝ℬoff 3.5 3.1 3.4 𝝉𝝉𝟏𝟏 𝒊𝒊 𝝉𝝉𝟐𝟐 𝒊𝒊 𝝉𝝉𝟏𝟏 𝒊𝒊 … 𝝉𝝉𝟐𝟐 𝒊𝒊 𝝉𝝉𝟑𝟑 𝒊𝒊 2.9 3.3 1.9 𝒊𝒊 … 𝝉𝝉 � 𝟏𝟏 𝝉𝝉 � 𝟐𝟐 𝝉𝝉 � 𝟑𝟑 4.3 2.4 1.2 1.3 1.9 2.9 2.4 3.4 2.3 𝑮𝑮 � 𝝉𝝉 � 𝟏𝟏 … 𝟏𝟏 − 𝜷𝜷 𝜷𝜷 𝑮𝑮 � 𝝉𝝉 � 𝟐𝟐 𝑮𝑮 � 𝝉𝝉 � 𝟑𝟑 𝑩𝑩 𝒊𝒊 𝐨𝐨𝐨𝐨 𝑩𝑩𝐨𝐨𝐨𝐨𝐨𝐨 Prioritized Sampling [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Considered long-horizon environments We evaluate SISL across four long-horizon, multi-task environments: Kitchen and Maze2D from Nam et al. (2022), and Office and AntMaze, newly introduced in this work, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Learning curves of the meta-train and meta-test phases on Kitchen ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of buffer mixing coefficient [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Component evaluation 0 0.1K 0.2K 0.3K 0.4K 0.5K Iteration 2.5 3.0 3.5 Test Average Return 0 0.1K 0.2K 0.3K 0.4K 0.5K Iteration 0.2 0.4 0.6 0.8 1.0 (a) Kitchen( =0.3) (b) Maze2D( =1.5) T = 0.1 T = 0.5 T = 1.0 T = 2.0 [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action sequences into reusable skills and employing hierarchical decision-making. However, these methods are highly susceptible to noisy offline demonstrations, leading to unstable skill learning and degraded performance. To address this, we propose Self-Improving Skill Learning (SISL), which performs self-guided skill refinement using decoupled high-level and skill improvement policies, while applying skill prioritization via maximum return relabeling to focus updates on task-relevant trajectories, resulting in robust and stable adaptation even under noisy and suboptimal data. By mitigating the effect of noise, SISL achieves reliable skill learning and consistently outperforms other skill-based meta-RL methods on diverse long-horizon tasks. Our code is available at https://epsilog.github.io/SISL.

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 / 0 minor

Summary. The paper proposes Self-Improving Skill Learning (SISL) to address noise sensitivity in skill-based meta-RL for long-horizon tasks. It introduces decoupled high-level and skill-improvement policies combined with maximum-return relabeling to enable self-guided refinement and prioritization of task-relevant trajectories from noisy offline demonstrations, claiming this yields reliable skill learning and consistent outperformance over prior skill-based meta-RL methods.

Significance. If the empirical claims hold, the work would offer a practical route to robust hierarchical meta-RL under imperfect data, a common real-world constraint. The public code release is a positive contribution that supports reproducibility.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'SISL achieves reliable skill learning and consistently outperforms other skill-based meta-RL methods' is presented without any experimental results, baselines, ablation studies, or statistical details visible in the manuscript. This absence prevents assessment of whether the decoupled policies and maximum-return relabeling actually mitigate noise as asserted.
  2. [Method (implied from abstract description)] The method description relies on the assumption that maximum-return relabeling (without ground-truth labels or clean data) will reliably identify and refine task-relevant skills. No analysis or safeguards are provided against the possibility that corrupted returns or early high-level selections could amplify rather than filter noise, which is load-bearing for the robustness claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below with clarifications from the full manuscript and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'SISL achieves reliable skill learning and consistently outperforms other skill-based meta-RL methods' is presented without any experimental results, baselines, ablation studies, or statistical details visible in the manuscript. This absence prevents assessment of whether the decoupled policies and maximum-return relabeling actually mitigate noise as asserted.

    Authors: The abstract is a concise summary; the full manuscript (Sections 4–5) contains the requested details: quantitative comparisons against skill-based meta-RL baselines, ablation studies isolating the decoupled policies and maximum-return relabeling, and statistical results (means and standard errors over 5–10 seeds) on long-horizon tasks with injected noise. These experiments directly support the claim that the components mitigate noise. We will revise the abstract to include a short clause referencing the empirical validation. revision: partial

  2. Referee: [Method (implied from abstract description)] The method description relies on the assumption that maximum-return relabeling (without ground-truth labels or clean data) will reliably identify and refine task-relevant skills. No analysis or safeguards are provided against the possibility that corrupted returns or early high-level selections could amplify rather than filter noise, which is load-bearing for the robustness claim.

    Authors: The paper presents empirical evidence that maximum-return relabeling improves performance under noisy demonstrations, but we agree that explicit analysis of failure modes (e.g., early mis-selection amplifying noise) is missing. We will add a short discussion subsection and potential safeguards (return thresholding, periodic re-evaluation of high-level selections) in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The provided abstract and context describe SISL as a proposed combination of decoupled high-level and skill-improvement policies with maximum-return relabeling to handle noisy offline data. No equations, self-definitions, fitted inputs renamed as predictions, or load-bearing self-citations are present that would reduce the claimed performance gains to the method's own inputs by construction. The central claims rest on the empirical effectiveness of the introduced components rather than any self-referential reduction, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the method appears to rest on standard RL assumptions (MDP formulation, policy gradient updates) that are not detailed here.

pith-pipeline@v0.9.0 · 5682 in / 1090 out tokens · 38119 ms · 2026-05-23T03:19:49.108094+00:00 · methodology

discussion (0)

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    is a skill-based meta-RL algorithm that uses both offline datasets and meta-train tasks. While it shares SISL’s approach of extracting reusable skills and performing meta- train and meta-test phases, SiMPL fixes the skill model without further updates during meta-training. SiMPL’s loss function is also detailed in Section 3, and SiMPL’s implementation use...

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    SISL consistently demonstrated superior robustness, outper- forming all baselines across various environments and noise levels

    Rows represent evaluation environments, and columns denote noise levels. SISL consistently demonstrated superior robustness, outper- forming all baselines across various environments and noise levels. At higher noise levels such as Noise(σ= 0.2), Noise(σ= 0.3) for Kitchen and Office, and Noise(σ= 1.0), Noise(σ= 1.5) for Maze2D and AntMaze, significant per...

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