Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
This paper explores using Reinforcement Learning with Verifiable Rewards (RLVR) to train small language models for tool-use in enterprise SaaS workflows, addressing the limitations of next-token prediction. In five synthetic environments simulating Jira and Confluence APIs, RL-trained models improved average reward from 0.35–0.92 to 0.95–1.00 on four non-degenerate scenarios, with the largest gain on Confluence page creation (0.35 to 1.00). Limitations include the scalability of hand-crafting rewards and a saturating reward in one scenario.
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[Submitted on 1 Jul 2026]
Title:Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
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Abstract:Large language models are trained to predict the next token, not to act inside a specific API. In niche enterprise SaaS workflows -- where success means hitting the right endpoint with the right nested arguments in the right order -- this objective mismatch shows up as silent failures: dropped required fields, hallucinated tools, or early stops after a single read. We ask whether Reinforcement Learning with Verifiable Rewards (RLVR), applied directly in the target environment, closes the gap. As a proof of concept we build a suite of five synthetic environments emulating the Jira REST v3 and Confluence v2 APIs at schema fidelity; rewards are computed entirely from the tool-call trace, with no live API, no learned judge, and no human label in the loop. Scoring prompted Qwen3-1.7B and Qwen3.5-4B on the same checkers that drive GRPO training, we find that on the four scenarios whose rewards are non-degenerate the RL-trained policy lifts average reward from a 4B-baseline range of 0.35--0.92 to 0.95--1.00, with the largest single gain on Confluence page creation ($0.35 \rightarrow 1.00$). We position this as a preliminary step toward outcome-optimised small models for niche enterprise APIs, and foreground two limitations a workshop reader should weigh: hand-crafting verifiable rewards does not scale beyond the handful of endpoints reported here, and one of our five scenarios (ticket-transition) has a saturating reward shape that the prompted 4B already maxes out.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01465 [cs.AI]
(or arXiv:2607.01465v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.01465
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Harshit Rajgarhia [view email] [v1] Wed, 1 Jul 2026 20:55:07 UTC (35 KB)
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