EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
arXiv:2606.04145v1 Announce Type: new Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et al. (2023) showed, this proxy diverges from world feedback (downstream eval metrics) under sustained optimization pressure, a phenomenon known as reward overoptimization. Existing platform schedulers ignore this divergence: non-clairvoyant schedulers optimize JCT without any quality signal, SLAQ-style quality-aware schedulers use training loss (a weaker proxy that drops monotonically through hacking)
The proliferation of cloud LLM fine-tuning platforms and the increasing reliance on RLHF for AI development necessitates robust mechanisms to ensure model quality and prevent reward overoptimization.
This development addresses a critical challenge in AI development where models can overoptimize for proxies, leading to a divergence from actual desired performance and potentially unreliable AI systems.
The ability to detect and correct reward overoptimization using 'world feedback' will enable more stable, reliable, and genuinely useful AI models, particularly in multi-tenant RLHF environments.
- · AI platform developers
- · Cloud LLM providers
- · Businesses deploying RLHF models
- · End-users of AI services
- · Platforms without robust quality control
- · Developers relying solely on proxy metrics
- · Inefficient RLHF methodologies
Improved reliability and performance of AI models fine-tuned with RLHF.
Increased trust and adoption of AI solutions due to more predictable and high-quality outputs.
Accelerated development and broader application of complex AI agents that require nuanced reward signals.
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Read at arXiv cs.LG