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arxiv: 2606.02355 · v1 · pith:XZAQNZDOnew · submitted 2026-06-01 · 💻 cs.AI · cs.LG

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

Zhongyu He , Yuanfan Li , Fei Huang , Tianyu Chen , Siyuan Chen , Xingyang Li , Meng Hsuan Yu , Xiangrong Liu
show 4 more authors
Leyi Wei Lu Pan Ke Zeng Xunliang Cai
This is my paper

Pith reviewed 2026-06-28 14:30 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords LLM agentsreinforcement learningskill internalizationself-miningALFWorldWebShopdistillationlong-horizon tasks
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The pith

SIRI lets LLM agents discover and internalize useful skills from their own successful trajectories without external generators or retrieval at inference.

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

The paper introduces a three-phase method for training LLM agents on long-horizon tasks. First the policy warms up with basic reinforcement learning to gather plain successful trajectories. It then mines compact skills by summarizing those trajectories and validates each candidate by running paired rollouts that either include or omit the summarized skill. Only skills that demonstrably raise success rates are distilled back into the base policy through trajectory-level utility and action-level advantage signals. At test time the agent operates with its original prompt alone, yet records higher success rates on ALFWorld and WebShop than prompt-only, standard RL, or memory-augmented baselines.

Core claim

SIRI is a three-phase process: policy warmup with GiGPO to collect skill-free trajectories, self-skill mining via summarization and paired validation, and distillation of beneficial skills into the base policy. This internalization leads to improved task success rates compared to baselines without requiring skill banks at runtime.

What carries the argument

Self-skill mining that validates summarized skills through paired skill-augmented and skill-free rollouts, then selectively distills beneficial action tokens using utility and advantage metrics.

If this is right

  • The policy improves success rates on long-horizon tasks such as ALFWorld and WebShop.
  • Inference runs with the original prompt only, leaving context length and latency unchanged.
  • Self-mining produces skills whose performance matches distillation performed by a closed-source large model.
  • The method outperforms prompt-based, standard RL, and memory-augmented baselines on the tested environments.

Where Pith is reading between the lines

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

  • The validation step could be repeated iteratively so the agent continues to bootstrap new skills from its own improving trajectories.
  • Because skill selection relies only on internal rollouts, the approach may transfer to environments where external skill libraries are unavailable or costly.
  • If the paired-rollout test proves stable across domains, it offers a route to reduce reliance on ever-larger external models for agent supervision.

Load-bearing premise

The paired skill-augmented versus skill-free rollout comparison reliably identifies beneficial skills without selection bias or reward hacking.

What would settle it

Running the full SIRI pipeline but replacing the validated skills with randomly chosen summaries from the same trajectories would yield no performance gain or even degradation on the benchmark tasks.

Figures

Figures reproduced from arXiv: 2606.02355 by Fei Huang, Ke Zeng, Leyi Wei, Lu Pan, Meng Hsuan Yu, Siyuan Chen, Tianyu Chen, Xiangrong Liu, Xingyang Li, Xunliang Cai, Yuanfan Li, Zhongyu He.

Figure 1
Figure 1. Figure 1: Conceptual comparison between (a) tradi [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SIRI. (a) Phase 0: The agent interacts with the environment to collect initial trajectory data for cold-start policy initialization. (b) Phase 1: Candidate skills are extracted into a structured SkillBank, followed by parallel rollouts with and without skills to compute the hindsight macro utility ∆g for dynamic skill evaluation. (c) Phase 2: The shared policy parameterizes validated skills via… view at source ↗
Figure 3
Figure 3. Figure 3: Training curves on ALFWorld and WebShop, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training success rate trajectories on WebShop, [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LLM-as-a-Judge quality scores for skills [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at inference, increasing engineering complexity, context length, and deployment latency. We propose Self-Internalizing Reinforcement learning with Intrinsic skills (SIRI), a three-phase framework that enables agents to discover, validate, and internalize skills without external skill generators or inference-time skill banks. SIRI first warms up the policy with GiGPO to acquire basic interaction ability and collect successful skill-free trajectories. It then performs self-skill mining, where the current policy summarizes compact skills from its own successful plain rollouts and validates them through paired skill-augmented and skill-free rollouts. Finally, SIRI distills only beneficial skill-guided action tokens into the plain policy using trajectory-level utility and action-level advantage. At inference, the agent runs with the original prompt only. On ALFWorld and WebShop with Qwen2.5-7B-Instruct, SIRI improves GiGPO from 0.908 to 0.930 on ALFWorld and from 0.728 to 0.813 on WebShop, outperforming prompt-based, RL-based, and memory-augmented baselines. Further analysis shows that our self-mining strategy can achieve performance comparable to distillation with closed-source large model. Our code is available at https://github.com/kirito618/SIRI.

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 / 1 minor

Summary. The paper introduces SIRI, a three-phase framework for LLM agents that warms up a policy with GiGPO on successful trajectories, performs self-skill mining by summarizing and validating skills via paired skill-augmented vs. skill-free rollouts, then distills only beneficial skills into the base policy via trajectory utility and action advantage. At inference the agent uses only the original prompt. On ALFWorld and WebShop with Qwen2.5-7B-Instruct it reports gains over GiGPO (0.908→0.930 and 0.728→0.813) and outperforms prompt, RL, and memory baselines; self-mining is claimed comparable to closed-source distillation.

Significance. If the reported gains are robust to rollout variance and selection effects, the method would demonstrate a practical route to internalizing reusable skills without external generators or inference-time retrieval, lowering deployment cost while preserving or improving performance. The self-mining result being comparable to closed-source models is a notable strength if substantiated.

major comments (2)
  1. [Abstract / self-skill mining description] The skill-validation step (self-skill mining phase) is load-bearing for the central claim that gains come from internalized beneficial skills rather than continued RL or cherry-picked trajectories. The manuscript supplies no details on the number of paired rollouts per candidate skill, the exact decision rule for “beneficial,” statistical testing, or correction for multiple comparisons; without these the paired comparison cannot be shown to isolate net-positive skills amid LLM rollout variance.
  2. [Results section (ALFWorld / WebShop tables)] Table or figure reporting the headline numbers (ALFWorld 0.930, WebShop 0.813) must include number of independent runs, standard errors or confidence intervals, and the precise validation criteria used to select which skills were distilled; the current numeric gains cannot be assessed for post-hoc bias or statistical reliability.
minor comments (1)
  1. The abstract states “our code is available” but does not specify the commit or exact reproduction instructions for the GiGPO baseline and the paired-rollout validation pipeline.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the self-skill mining validation and statistical reporting. We address each major comment below and will revise the manuscript to provide the requested details and clarifications.

read point-by-point responses

  1. Referee: [Abstract / self-skill mining description] The skill-validation step (self-skill mining phase) is load-bearing for the central claim that gains come from internalized beneficial skills rather than continued RL or cherry-picked trajectories. The manuscript supplies no details on the number of paired rollouts per candidate skill, the exact decision rule for “beneficial,” statistical testing, or correction for multiple comparisons; without these the paired comparison cannot be shown to isolate net-positive skills amid LLM rollout variance.

    Authors: We agree that the manuscript would benefit from a more explicit description of the validation protocol. In the revised manuscript we will add a dedicated paragraph in the self-skill mining subsection that states the number of paired rollouts performed per candidate skill, the precise decision rule applied to classify a skill as beneficial (trajectory utility and action advantage thresholds), and any statistical considerations used. We will also clarify that the mining phase is exploratory and therefore does not apply formal multiple-comparison corrections, while noting the potential implications for variance in LLM rollouts. These additions will allow readers to evaluate the isolation of net-positive skills more rigorously. revision: yes

  2. Referee: [Results section (ALFWorld / WebShop tables)] Table or figure reporting the headline numbers (ALFWorld 0.930, WebShop 0.813) must include number of independent runs, standard errors or confidence intervals, and the precise validation criteria used to select which skills were distilled; the current numeric gains cannot be assessed for post-hoc bias or statistical reliability.

    Authors: We acknowledge that the current tables lack the requested statistical context. In the revision we will augment the ALFWorld and WebShop result tables with the number of independent runs performed, standard errors or confidence intervals, and an explicit statement of the validation criteria (trajectory utility and action advantage thresholds) used to select skills for distillation. This will enable a clearer assessment of the reported gains and reduce concerns about post-hoc selection effects. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation is self-contained

full rationale

The paper describes a three-phase empirical pipeline (GiGPO warmup to collect trajectories, self-skill mining from successful rollouts, paired rollout validation, and distillation of beneficial skills) whose performance gains on ALFWorld and WebShop are reported as measured outcomes rather than derived quantities. No equations appear that equate a prediction to its own fitted input by construction, no self-citation is invoked as a uniqueness theorem or load-bearing premise, and no ansatz or renaming reduces the central claim to prior inputs. The method remains externally falsifiable via the reported benchmark comparisons.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities are stated. The framework implicitly assumes that successful trajectories contain extractable compact skills and that paired rollout comparison is an unbiased validator.

pith-pipeline@v0.9.1-grok · 5826 in / 1188 out tokens · 20617 ms · 2026-06-28T14:30:55.541301+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. UCOB: Learning to Utilize and Evolve Agentic Skills via Credit-Aware On-Policy Bidirectional Self-Distillation

    cs.AI 2026-06 unverdicted novelty 5.0

    UCOB improves agentic RL by using return-to-go comparisons between skill-conditioned and no-skill prompts as local teachers for bidirectional self-distillation and skill memory updates.

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