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arxiv: 2606.04815 · v2 · pith:VDIC44NDnew · submitted 2026-06-03 · 💻 cs.LG · cs.AI

Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents

Bo Mao , Jie Zhou , Yutao Yang , Xin Li , Xian Wei , Qin Chen , Xingjiao Wu , Liang He This is my paper

Pith reviewed 2026-06-28 07:03 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords lifelong learningLLM agentstest-time learningskill extractionreinforcement learningonline adaptationverifier-guided learning
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0 comments X

The pith

LifeSkill lets LLM agents extract skills from test-time rollouts and internalize them into parameters rather than retrieving experiences.

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

Existing lifelong learning agents for LLMs depend on discrete skill retrieval with static parameters at inference, which blocks continuous adaptation from test-time feedback. The paper proposes LifeSkill as a two-stage reinforcement learning framework whose first stage, Verifier-Guided Skill Learning, rewards candidate skills by the average verifier success across multiple skill-conditioned policy rollouts. The second stage, Online Skill Internalization, converts the resulting trajectories into reward signals that update the policy model directly during interaction. Experiments on LifelongAgentBench report a 7-point average performance gain over existing lifelong agent baselines.

Core claim

LifeSkill is a two-stage reinforcement learning framework in which Verifier-Guided Skill Learning rewards candidate skills according to the average verifier success of multiple skill-conditioned policy rollouts, and Online Skill Internalization continuously improves the policy model at test time by transforming skill-conditioned trajectories into reward signals, thereby enabling the agent to internalize reasoning capabilities into its parameters and avoid context bloat from experience retrieval.

What carries the argument

Verifier-Guided Skill Learning that supplies a training signal for skill extraction via average verifier success on multiple rollouts, combined with Online Skill Internalization that turns trajectories into parameter-updating rewards.

If this is right

  • Agents can improve long-horizon performance without the context bloat that comes from retrieving past experiences.
  • Extracted skills are more likely to be useful for solving tasks because they are selected by empirical rollout success.
  • The policy model receives direct parameter updates from test-time interaction rather than remaining static.
  • Average performance on LifelongAgentBench rises by 7 absolute points relative to retrieval-based lifelong baselines.

Where Pith is reading between the lines

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

  • The same verifier-plus-internalization loop could be tested in environments whose dynamics change over time to measure robustness to distribution shift.
  • If the approach generalizes, agents might accumulate increasingly general internal capabilities across successive tasks without external memory growth.
  • Replacing the verifier with a learned critic trained on the same rollouts might reduce dependence on an external success signal.
  • Applying the framework to multi-agent settings could let agents co-evolve shared skills through joint rollouts.

Load-bearing premise

The average verifier success across multiple skill-conditioned policy rollouts supplies a reliable training signal for skills that are actually useful rather than merely textually plausible.

What would settle it

An ablation experiment in which skills selected without the verifier signal produce equivalent or higher downstream task success rates than verifier-selected skills would falsify the claim that the verifier provides a useful training signal.

Figures

Figures reproduced from arXiv: 2606.04815 by Bo Mao, Jie Zhou, Liang He, Qin Chen, Xian Wei, Xingjiao Wu, Xin Li, Yutao Yang.

Figure 1
Figure 1. Figure 1: Comparison of all methods on LifelongAgentBench. Left of the dashed line: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of LifeSkill. Verifier-Guided Skill Learning optimizes the extractor using the average verifier success of these skill-conditioned executions, while Online Skill Internalization removes explicit skill text from verified successful trajectories and updates the Policy Model under the original task input alone. 3 Our LifeSkill Method We study online lifelong learning over a stream of tasks and instan… view at source ↗
Figure 3
Figure 3. Figure 3: Hyperparameter analysis of LifeSkill. Scaling Test-Time Compute. We study the effect of test-time compute by varying the number of sampled candidate skills Ks and skill-conditioned trajectories Kτ. For simplicity, we set Ks = Kτ and vary them jointly from 1 to 5. As shown in Figure 3a, increasing the rollout budget consistently improves performance on both DB and OS. On DB, accuracy 9 [PITH_FULL_IMAGE:fig… view at source ↗
read the original abstract

Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learning agents for long-horizon tasks typically depend on discrete skill or past experiences retrieval with static parameters during inference, which prevents them from continuously internalizing test-time feedback like human learners. To bridge this gap, we propose Skill-enhanced Test-Time Co-Evolution (\texttt{LifeSkill}), a two-stage reinforcement learning framework for Online Lifelong Learning Agents. Specifically, we design Verifier-Guided Skill Learning that addresses the lack of direct supervision for skill extraction by rewarding candidate skills according to the average verifier success of multiple skill-conditioned policy rollouts, encouraging the model to generate skills that are useful for solving tasks rather than merely plausible in text. Furthermore, we introduce Online Skill Internalization, which continuously improves the policy model during test-time interaction by transforming skill-conditioned trajectories into reward signals. This enables the agent to directly internalize reasoning capabilities into its parameters, avoiding the context bloat of experience retrieval. Experiments on LifelongAgentBench show that LifeSkill improves average performance by 7 absolute points by comparing with existing lifelong agent baselines.

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 LifeSkill, a two-stage RL framework for online lifelong learning LLM agents. It consists of Verifier-Guided Skill Learning, which extracts skills by rewarding candidates according to the average verifier success rate across multiple skill-conditioned policy rollouts, and Online Skill Internalization, which converts skill-conditioned trajectories into reward signals to update the policy parameters during test-time interaction. The central empirical claim is a 7 absolute point average performance gain on LifelongAgentBench relative to existing lifelong agent baselines.

Significance. If the empirical result holds after addressing the verifier-signal concern, the framework could meaningfully advance test-time adaptation for agents by enabling direct parameter internalization rather than retrieval-based methods. The verifier-guided reward addresses the lack of direct supervision for skill extraction, and the approach avoids context bloat. However, the absence of ablations, error bars, implementation details on reward stabilization, and independent validation of skill utility limits the assessed significance at present.

major comments (2)
  1. [Abstract (Verifier-Guided Skill Learning)] Abstract, Verifier-Guided Skill Learning paragraph: the central 7-point gain on LifelongAgentBench rests on the assumption that average verifier success over skill-conditioned rollouts supplies a reliable causal training signal. No description is given of how the verifier is constructed, whether it shares parameters or training distribution with the policy, or any independent (human/oracle) validation of extracted skill utility; this leaves open the possibility that reported gains reflect verifier-policy alignment on surface patterns rather than transferable capability.
  2. [Abstract (experiments and Online Skill Internalization)] Abstract, Online Skill Internalization paragraph and experiments: the manuscript provides no ablation results, no error bars, and no details on how trajectories are converted into stable reward signals or how the two stages interact during online operation. Without these, the contribution of each component to the 7-point delta cannot be isolated, undermining the claim that the framework enables continuous internalization.
minor comments (2)
  1. [Abstract] The abstract does not specify the base models, number of rollouts per skill, or exact LifelongAgentBench tasks used; these details are needed for reproducibility even if full implementation is in the body.
  2. [Abstract] Notation for the two-stage process and reward formulation is introduced only at a high level; clearer pseudocode or equations would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We address each major comment below and will incorporate the necessary revisions into the manuscript.

read point-by-point responses

  1. Referee: [Abstract (Verifier-Guided Skill Learning)] Abstract, Verifier-Guided Skill Learning paragraph: the central 7-point gain on LifelongAgentBench rests on the assumption that average verifier success over skill-conditioned rollouts supplies a reliable causal training signal. No description is given of how the verifier is constructed, whether it shares parameters or training distribution with the policy, or any independent (human/oracle) validation of extracted skill utility; this leaves open the possibility that reported gains reflect verifier-policy alignment on surface patterns rather than transferable capability.

    Authors: We thank the referee for this comment. We will revise the abstract and methods section to include a full description of how the verifier is constructed, its parameter sharing (or lack thereof), and training distribution. Regarding independent validation, the manuscript does not include human or oracle validation of skill utility beyond the verifier, and we will add this as an explicit limitation while arguing that the gains on held-out tasks support the claim of transferable capability rather than surface alignment. We believe the current results provide initial evidence, and additional validation can be considered in future work. revision: yes

  2. Referee: [Abstract (experiments and Online Skill Internalization)] Abstract, Online Skill Internalization paragraph and experiments: the manuscript provides no ablation results, no error bars, and no details on how trajectories are converted into stable reward signals or how the two stages interact during online operation. Without these, the contribution of each component to the 7-point delta cannot be isolated, undermining the claim that the framework enables continuous internalization.

    Authors: We agree that these details are necessary for a complete evaluation. In the revised version, we will include ablation studies that isolate the contribution of Verifier-Guided Skill Learning and Online Skill Internalization, report error bars based on multiple random seeds, and provide implementation details on converting trajectories to reward signals (using the average verifier success rate as the reward) and the online interaction between the two stages. These additions will allow readers to assess the individual contributions to the observed 7-point improvement. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical framework with independent benchmark results

full rationale

The paper proposes a two-stage RL framework (Verifier-Guided Skill Learning and Online Skill Internalization) and reports an empirical 7-point gain on LifelongAgentBench against baselines. No equations, fitted parameters presented as predictions, self-definitional quantities, or load-bearing self-citation chains appear in the text. The performance delta is framed as an external benchmark comparison rather than a derivation that reduces to its own inputs by construction. The method is self-contained against the stated benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

Abstract introduces two new named components but provides no numerical free parameters, no explicit axioms, and no invented physical or mathematical entities; information is too high-level to populate the ledger further.

invented entities (2)
  • Verifier-Guided Skill Learning no independent evidence
    purpose: Supply training signal for skill extraction without direct supervision
    New named stage introduced to solve the lack of supervision for skills.
  • Online Skill Internalization no independent evidence
    purpose: Convert test-time trajectories into parameter updates
    New named stage for continuous internalization during interaction.

pith-pipeline@v0.9.1-grok · 5756 in / 1179 out tokens · 20827 ms · 2026-06-28T07:03:00.565996+00:00 · methodology

discussion (0)

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Reference graph

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