Papers with multi-turn reinforcement learning problem

1 papers
TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards (2026.acl-long)

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Challenge: Existing approaches to training multi-turn attackers to probe model safety vulnerabilities rely on turn-level optimization, which is insufficient for learning long-term attack strategies.
Approach: They propose a multi-turn reinforcement learning problem that optimizes the harmfulness of the final-turn response as the outcome reward.
Outcome: The proposed approach improves attack success rates across multiple models and benchmarks, highlighting the effectiveness of the proposed approach.

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