Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
arXiv:2607.01465v1 Announce Type: new 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 synthet
The rapid development of large language models is pushing the boundaries of their application, revealing the limitations of next-token prediction for complex, goal-oriented tasks.
This research directly addresses a critical hurdle in deploying AI agents for automating enterprise workflows, potentially unlocking significant productivity gains.
The focus for AI agent development shifts from pure language generation to incorporating verifiable reinforcement learning within target environments for reliable task execution.
- · SaaS providers with complex APIs
- · Enterprises adopting AI for workflow automation
- · Researchers in reinforcement learning
- · Vendors offering purely next-token prediction based automation solutions
- · Manual workflow process industries
Enterprise AI agents become significantly more reliable and effective at automating intricate business processes.
A new wave of 'agent-native' SaaS applications emerges, designed from the ground up for AI agent interaction rather than human users.
The definition of 'software developer' evolves to include expertise in designing and training RLVR agents for API interaction.
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Read at arXiv cs.AI