DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter
Reinforcement learning with verifiable rewards (RLVR) for multi-turn agents relies on sparse terminal rewards that say little about individual steps. Within-group step comparisons depend on reliable state matching and sufficiently diverse sampled outcomes, while direct step scoring may require costly external evaluators, human annotations, or task-specific heuristics. We introd
Reading in Plain English mode — toggle below to switch
Reinforcement learning with verifiable rewards (RLVR) for multi-turn agents relies on sparse terminal rewards that say little about individual steps. Within-group step comparisons depend on reliable state matching and sufficiently diverse sampled outcomes, while direct step scoring may require costly external evaluators, human annotations, or task-specific heuristics. We introd
This work introduces a novel approach to improve multi-turn agent training by creating a reusable progress signal, which can significantly reduce the reliance on costly external evaluators or human annotations. For ML engineers, understanding how to generate and utilize such internal progress metrics is crucial for developing more efficient and scalable reinforcement learning systems for conversational AI and autonomous agents.