CTRL-RAG: A Novel Approach to Reinforcement Learning for RAG

The increasing adoption of RAG (Retrieval-Augmented Generation) models requires advanced training techniques to ensure context-sensitive reasoning and faithful generations. A new study introduces CTRL-RAG, a reinforcement learning (RL) framework that aims to overcome the limitations of existing approaches.

Overcoming the limitations of external reward systems

Traditional RL methods for RAG often rely on external rewards that struggle to accurately evaluate document faithfulness and can generate incorrect assessments in open-domain contexts. CTRL-RAG introduces a hybrid "internal-external" reward system based on a Contrastive Likelihood Reward (CLR). This system optimizes the log-likelihood gap between responses conditioned on prompts with and without supporting evidence.

Benefits of Contrastive Likelihood Reward (CLR)

CLR encourages the model to extract relevant evidence and increases its confidence when grounded in a specific context. This mechanism aims to reduce hallucinations and improve the overall quality of the generations. Experimental results demonstrate that CTRL-RAG, used alone or in combination with external rewards, delivers high performance in single-hop, multi-hop, and vertical-domain benchmarks.

Next steps

The training code and models will be released soon.