Proxy State Evaluation: Scaling Verifiable Rewards for AI Agents

As AI agents become increasingly capable of executing multi-step tasks involving tool use and complex decision-making, researchers face a fundamental challenge: how do you verify that an agent performed well when the evaluation itself becomes prohibitively expensive at scale? A new research paper addresses this critical bottleneck with a novel approach to reward verification in tool-calling language model agents.

The Verification Bottleneck in Agentic AI

Modern LLM-based agents don't just generate text—they interact with external tools, APIs, databases, and services across multiple conversational turns. Each interaction creates state changes that affect subsequent decisions. Training these agents through reinforcement learning requires reward signals that accurately reflect task completion, but verifying success in complex multi-turn scenarios presents significant scalability challenges.

Traditional approaches to reward modeling often rely on human evaluation or expensive automated verification systems that don't scale efficiently. When an agent completes a ten-step workflow involving multiple API calls, file operations, and data transformations, determining whether the outcome genuinely satisfies the original intent requires deep contextual understanding of the entire trajectory.

Proxy State-Based Evaluation: A Scalable Solution

The research introduces proxy state-based evaluation as a mechanism for generating verifiable rewards without requiring exhaustive end-state verification. Instead of evaluating only the final outcome, this approach tracks intermediate proxy states throughout the agent's execution trajectory.

The key insight is that certain observable states during task execution serve as reliable indicators of correct behavior. By identifying and monitoring these proxy states, the system can construct verifiable reward signals that scale more efficiently than full trajectory evaluation while maintaining strong correlation with actual task success.

How Proxy States Work

Consider an agent tasked with booking a flight and hotel for a business trip. The traditional approach would verify the final booking confirmations. The proxy state approach instead identifies intermediate verification points:

• Did the agent correctly parse the travel dates from the user request?
• Did the API calls to the flight service contain valid parameters?
• Did the agent maintain consistency between flight arrival time and hotel check-in?
• Were error states from tool calls handled appropriately?

Each proxy state represents a verifiable checkpoint that's cheaper to evaluate than the full task outcome but strongly predicts successful completion.

Technical Architecture and Implementation

The evaluation framework operates on several technical principles that enable scalability:

State Abstraction: Rather than tracking complete system states—which can be computationally expensive—the method extracts relevant features that capture task-critical information. This abstraction reduces the dimensionality of what needs to be verified.

Compositional Verification: Complex tasks decompose into verifiable sub-components. The reward signal aggregates from multiple proxy state evaluations, each handling a manageable verification scope.

Temporal Credit Assignment: By tracking proxy states across the interaction timeline, the system can attribute rewards to specific actions rather than only terminal states. This addresses the classic credit assignment problem in reinforcement learning for sequential decision-making.

Implications for AI Agent Training

This research addresses a fundamental bottleneck in developing more capable AI agents. Current systems like computer-use agents and coding assistants that interact with external tools require massive amounts of training signal. Making reward verification scalable directly impacts how quickly these systems can improve.

The approach also has implications for AI safety and alignment. Verifiable rewards mean more transparent training objectives—we can inspect what behaviors the system is actually being rewarded for. This interpretability matters as agents gain access to more powerful tools and operate with greater autonomy.

Connection to Synthetic Media and Content Generation

For the AI video and synthetic media space, agentic AI represents the next frontier. Future content creation workflows will involve agents that autonomously select assets, edit footage, apply effects, and iterate based on feedback—all through multi-turn tool interactions.

Training such agents requires exactly the kind of scalable reward verification this research enables. An AI video editing agent might need rewards based on proxy states like correct timeline positioning, appropriate transition selection, audio-visual synchronization, and style consistency—rather than requiring human evaluation of every generated clip.

The Broader Agentic AI Landscape

This work joins a growing body of research on reliable agent evaluation. Previous studies have examined information fidelity in tool-using agents through martingale analysis and explored memory architectures for personalized agentic systems. Together, these advances push toward AI agents that can be trusted to execute complex workflows autonomously.

The scalable verification challenge isn't unique to any single application domain—it's a fundamental requirement for deploying capable agents in production environments where training must continue to improve system capabilities over time.

Stay informed on AI video and digital authenticity. Follow Skrew AI News.