BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning
Quick summary
arXiv:2609.36505v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) with verifiable rewards improves the ability of large language models (LLMs) to tackle knowledge-intensive tasks by learning to interleave search and reasoning. However, most existing ARL methods optimize only LLM-generated tokens and treat retrieved evidence as environment observations. This creates an information-credit gap: failures caused by missing or misleading evidence are attributed to the LLM policy rather than to the retriever, which motivates training the LLM and the retriever jointly. In this paper
Key takeaways
- arXiv:2609.36505v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) with verifiable rewards improves the ability of large language models (LLMs) to tackle knowledge-intensive tasks by learning to interleave search and reasoning.
- However, most existing ARL methods optimize only LLM-generated tokens and treat retrieved evidence as environment observations.
- This creates an information-credit gap: failures caused by missing or misleading evidence are attributed to the LLM policy rather than to the retriever, which motivates training the LLM and the retriever jointly.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning” may reshape data collection, model training, output accountability and market access.
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