{"id":"0ab47b73-bb4f-4a6e-877a-007f254fffc5","arxiv_id":"2605.10923","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"SLIM dynamically optimizes the active external skill set in agentic RL via leave-one-skill-out marginal contribution estimates and lifecycle operations, delivering a 7.1% average gain over baselines on ALFWorld and SearchQA while showing some skills remain externally useful.","lead":"The paper introduces SLIM, a framework that dynamically manages external skills for LLM agents during reinforcement learning by estimating each skill's marginal contribution and applying retain, retire, or expand operations. This challenges the view that skills must either persist indefinitely or be fully internalized into the policy.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Retirement of skills is interpreted as absorption into policy without direct verification that the policy has internalized the capability.","rationale":"The reader's weakest assumption correctly flags the non-monotonic dynamic set as central, but the load-bearing gap for the strongest claim is downstream: the empirical support for the 'not mutually exclusive' conclusion. The outperformance number could still be real even if the absorption interpretation is weaker; hence CONDITIONAL rather than REJECT. Agreement is partial because the reader's point and this concern both target the dynamic-management justification but at different layers (optimality vs. evidence of mechanism).","tokens_in":1749,"tokens_out":390,"duration_ms":24570,"concrete_test":"After a skill is retired in a trained SLIM agent, run an ablation that re-inserts the identical external skill and measures performance delta on held-out episodes that previously triggered that skill; also evaluate the policy alone on synthetic sub-tasks designed to require only that skill. If re-insertion yields no gain and the policy succeeds on the sub-tasks, absorption is supported; otherwise the retirement does not demonstrate internalization.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim that policy learning and external skill retention are not mutually exclusive rests on SLIM's lifecycle operations: skills are retired when leave-one-skill-out validation shows negligible marginal contribution after exposure. The paper treats this retirement as evidence that the skill has been absorbed into the parametric policy. However, retirement could instead reflect task completion, distribution shift, or the agent learning a different strategy that bypasses the skill, without any actual internalization. No direct test (e.g., probing the policy on skill-specific sub-tasks in isolation) is described to distinguish absorption from these alternatives. This makes the supporting interpretation of the 7.1% gain and the 'more general paradigm' claim rest on an unverified causal link between observed retirement and policy absorption.","agreement_with_reader":"partial"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes SLIM, a framework for dynamic Skill LIfecycle Management in agentic reinforcement learning. It models the active external skill set as a jointly optimized variable with policy learning, using leave-one-skill-out validation to estimate each skill's marginal contribution and applying retain, retire, and expand operations. The work reports that SLIM achieves a 7.1 percentage point average improvement over the best baselines on ALFWorld and SearchQA, and concludes that policy learning and external skill retention are not mutually exclusive, with some skills absorbed into the policy while others retain external value.","tokens_in":1934,"tokens_out":380,"duration_ms":18440,"significance":"If the experimental claims are substantiated with full protocols and verification, the result would support a more flexible paradigm for skill-based agentic RL that avoids the restrictive assumptions of either permanent external skill accumulation or complete internalization. This could inform more adaptive agent designs under limited parametric capacity, particularly for tasks where optimal skill sets are task- and stage-dependent.","major_comments":[{"comment":"Abstract: the reported 7.1% average improvement is presented without any description of the experimental protocol, baseline implementations, number of runs, statistical tests, or error bars, so the central performance claim cannot be evaluated or reproduced from the manuscript.","section":"Abstract"},{"comment":"Abstract: the claim that skill retirement demonstrates absorption into the parametric policy (supporting the non-mutually-exclusive relationship) is not directly tested; retirement after negligible leave-one-skill-out contribution could instead arise from task completion, distribution shift, or alternative strategies, and no probing of the policy on isolated skill-specific sub-tasks is described to distinguish these cases.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the thoughtful and constructive comments. We address each major point below, proposing targeted revisions to enhance clarity and strengthen the evidential basis of our claims while preserving the core contributions of the work.","responses":[{"response":"We agree that the abstract's brevity leaves the central claim difficult to evaluate in isolation. The full experimental protocol, including baseline implementations (ReAct, Reflexion, and skill-augmented variants), 5 independent runs per condition, paired t-tests for significance, and error bars, is detailed in Section 4 and Appendix B. In the revised manuscript we will expand the abstract with a concise clause summarizing the evaluation setup and number of runs, while retaining the word limit, and ensure the main text explicitly cross-references these details.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the reported 7.1% average improvement is presented without any description of the experimental protocol, baseline implementations, number of runs, statistical tests, or error bars, so the central performance claim cannot be evaluated or reproduced from the manuscript."},{"response":"This observation is correct: our current support for absorption is inferential, resting on the leave-one-skill-out marginal contribution dropping to negligible levels after policy updates while overall task performance is maintained. Alternative explanations such as task completion or distribution shift cannot be ruled out without additional controls. We will therefore add a new analysis subsection that probes the updated policy on isolated skill-specific sub-tasks for a representative sample of retired skills, comparing success rates before and after retirement to provide more direct evidence of internalization versus other factors.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the claim that skill retirement demonstrates absorption into the parametric policy (supporting the non-mutually-exclusive relationship) is not directly tested; retirement after negligible leave-one-skill-out contribution could instead arise from task completion, distribution shift, or alternative strategies, and no probing of the policy on isolated skill-specific sub-tasks is described to distinguish these cases."}],"tokens_in":1403,"tokens_out":443,"duration_ms":35383,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway here is that SLIM treats the set of active external skills as a dynamic variable that gets updated during policy learning, using leave-one-skill-out checks to trigger retain, retire, or expand operations. This seems like a reasonable extension beyond the usual accumulate-or-internalize assumptions.","headline":"SLIM adds dynamic retain-retire-expand operations for external skills based on leave-one-out marginals, but the absorption interpretation lacks direct checks.","tokens_in":2402,"tokens_out":132,"would_cite":false,"duration_ms":27935,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"SLIM estimates each active skill’s marginal external contribution through leave-one-skill-out validation, then applies three lifecycle operations: retaining high-value skills, retiring skills whose contribution becomes negligible after sufficient exposure, and expanding the skill bank"},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/RealityFromDistinction.lean","rs_theorem":"reality_from_one_distinction","paper_passage":"the optimal active skill set is non-monotonic, task- and stage-dependent"}],"headline":"SLIM skill-lifecycle management lies outside RS forcing chain","alignment":"orthogonal","rationale":"The paper's core machinery (leave-one-skill-out marginal contribution estimation, retain/retire/expand rules on active skill set A_t, non-monotonic trajectory under finite capacity constraint Eq. (2)) operates entirely in the domain of empirical agentic RL and prompt-based skill banks. It invokes no recognition cost J(x), no φ-ladder, no 8-tick periodicity, and no parameter-free derivation of constants. RS theorems such as reality_from_one_distinction, washburn_uniqueness_aczel, and alexander_duality_circle_linking therefore neither confirm nor contradict the reported 7.1 pp gains or the absorption-vs-retirement interpretation.","tokens_in":60425,"confidence":"high","tokens_out":342,"duration_ms":10761,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"SLIM dynamically manages external skills in agentic RL by retaining high-value ones, retiring low-contribution ones, and adding new ones as needed.","keywords":["dynamic skill management","agentic reinforcement learning","external skills","large language model agents","skill lifecycle","leave-one-skill-out validation","policy learning"],"falsifier":"Running the same ALFWorld and SearchQA experiments with a fixed or monotonically accumulating skill set and finding equal or higher average performance than SLIM would show the dynamic lifecycle operations are not necessary.","tokens_in":2657,"feed_emoji":"🤖","tokens_out":678,"duration_ms":36416,"temperature":0.7,"pith_summary":"The paper claims that the best set of external skills for an agent is not fixed or ever-growing but changes over training stages and across tasks because of limited model capacity. Existing approaches either keep all skills forever or try to internalize everything, which the authors say is too restrictive. SLIM instead treats the active skill set as something to optimize jointly with the policy, using leave-one-skill-out checks to measure each skill's current value. This leads to three operations that keep the skill bank efficient while still allowing the policy to learn. Experiments on ALFWorld and SearchQA show this yields higher success rates than baselines that follow the older assumptions.","feed_headline":"Dynamic skill management lifts agent performance by 7.1 points","feed_subtitle":"SLIM retires low-value skills and adds new ones during training instead of keeping every skill or forcing full internalization.","key_machinery":"Leave-one-skill-out validation to measure marginal contribution of each active skill, which then triggers retain, retire, or expand decisions on the skill set during joint policy training.","core_discovery":"SLIM treats the active external skill set as a dynamic optimization variable jointly updated with policy learning. It estimates each active skill's marginal external contribution through leave-one-skill-out validation, then applies three lifecycle operations: retaining high-value skills, retiring skills whose contribution becomes negligible after sufficient exposure, and expanding the skill bank when persistent failures reveal missing capability coverage.","pith_inferences":["The same retain-retire-expand logic could be tested in other tool-using agent settings where the total number of available skills grows large.","Periodic skill-value audits might reduce memory and compute costs in long-horizon agent training without sacrificing final performance.","The non-monotonic skill-set pattern suggests that lifelong agent learning may benefit from explicit retirement mechanisms rather than only addition or compression."],"forward_implications":["SLIM outperforms the best baselines by an average of 7.1 percentage points across ALFWorld and SearchQA.","Policy learning and external skill retention are compatible: some skills become absorbed into the policy while others continue to deliver value when kept external.","Dynamic management of the active skill set provides a more general paradigm for skill-based agentic RL than methods that assume skills either accumulate persistently or are fully internalized."],"fun_headline_variants":["SLIM treats active external skills as dynamic optimization variables","Lifecycle operations retain retire and expand skills in agent RL","Marginal contributions guide skill retention via validation in SLIM","Dynamic updates to skill sets during policy learning with SLIM"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Because of limited parametric capacity and uneven contributions across skills, the best active skill set changes over time and depends on the specific task and training stage.","fun_headline_variants_meta":{"raw":{"variants":["SLIM treats active external skills as dynamic optimization variables","Lifecycle operations retain retire and expand skills in agent RL","Marginal contributions guide skill retention via validation in SLIM","Dynamic updates to skill sets during policy learning with SLIM"]},"model":"grok-4.3","cost_usd":0.00822,"raw_usage":{"total_tokens":3654,"prompt_tokens":678,"num_sources_used":0,"completion_tokens":63,"cost_in_usd_ticks":82203000,"prompt_tokens_details":{"text_tokens":678,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2913,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":678,"tokens_out":63,"duration_ms":38050,"temperature":1.0,"reasoning_tokens":2913,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-20T22:16:21.896588+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same ALFWorld and SearchQA experiments with a fixed or monotonically accumulating skill set and finding equal or higher average performance than SLIM would show the dynamic lifecycle operations are not necessary.","supporting_citations":[],"review_version":2}