pith:WFKUFZ5S
Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning
Agentic RL agents improve when the active external skill set is treated as a dynamic optimization variable updated jointly with policy learning.
arxiv:2605.10923 v2 · 2026-05-11 · cs.LG · cs.CL
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Claims
SLIM treats the active external skill set as a dynamic optimization variable jointly updated with policy learning. Experiments show that SLIM outperforms the best baselines by an average of 7.1% points across ALFWorld and SearchQA. Results further indicate that policy learning and external skill retention are not mutually exclusive.
The assumption that the optimal active skill set is non-monotonic, task- and stage-dependent, and that leave-one-skill-out validation can reliably estimate each skill's marginal external contribution without introducing bias or prohibitive cost.
SLIM dynamically optimizes active external skills in agentic RL via leave-one-skill-out marginal contribution estimates and three lifecycle operations, outperforming baselines by 7.1% on ALFWorld and SearchQA while showing some skills are internalized and others remain external.
Receipt and verification
| First computed | 2026-05-20T00:03:17.205903Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b15542e7b2ca3643dc65bd67b97abceadc7fb1324703cc5520728d550027df9c
Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/WFKUFZ5SZI3EHXDFXVT3S6V45L \
| jq -c '.canonical_record' \
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Canonical record JSON
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