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DeltaTTG uses token-to-go estimates to shape multi-turn agent training

huggingface.co by Xuyan Ye et al. Editor score 8/10

DeltaTTG trains a reusable progress meter from recorded trajectories and uses changes in its estimates to redistribute outcome-derived advantages across steps; it improves average scores over GRPO by 5.7 and 7.2 points on two model families. The method provides a progress signal without task-specific rules or external step evaluators.

What

- DeltaTTG trains a reusable progress meter for multi-turn agents using Token-to-Go, the tokens remaining until task completion, as its supervision target.

Method

- During actor training, changes in the frozen meter’s estimates redistribute outcome-derived advantages across steps without changing rewards or trajectory advantage signs.

Why it matters

- It improves average scores over GRPO by 5.7 points with Qwen3-4B and 7.2 points with Llama-3.1-8B-Instruct, while keeping actor-training GPU-hours close to GRPO.

Read on huggingface.co → Code · 1 stars

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