arXiv:2607.02781v1 Announce Type: cross Abstract: Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates. However, existing inference-time alignment methods typically optimize a single scalar score, so explicit safety constraints must either be ignored or encoded through manually tuned penalties. We propose Lagrangian Reward Augmentation (LARA), a general inference-time alignment framework under safety constraints. Starting from a KL-regularized constrained objective with a reward model and a cost mod
The increasing deployment of large language models in sensitive applications necessitates robust safety mechanisms, which this research directly addresses.
This development allows for more controlled and safer AI deployments without repetitive training costs, enabling broader and more trustworthy application of advanced AI.
The ability to enforce explicit safety constraints during inference-time alignment for language models significantly enhances their reliability and ethical deployment.
- · AI developers
- · Organizations deploying LLMs
- · AI safety researchers
- · Regulators
- · AI systems lacking robust safety mechanisms
Wider adoption and trust in AI systems due to improved safety and alignment.
Reduced incidence of harmful or biased AI outputs, leading to fewer PR crises for deploying entities.
Accelerated development of AI agents operating with higher levels of autonomy in sensitive domains.
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Read at arXiv cs.AI