TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents
Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training. Outcome rewards provide reliable supervision for short-horizon reasoning, but become sparse and high-variance as trajectories grow to tens or hundreds of tool calls. They can also be misleading: a failed rollout may contain many useful actions that move the agent closer to the goal, yet outcome-only training assigns them the same negative advantage as the eventual mistake. We propose TRACE (Turn-level Rewar
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- PossiblePossibly related (embedding) · 54%Best practices for multi-turn reinforcement learning in Amazon SageMaker AI →
- PossiblePossibly related (embedding) · 49%Production-grade AI agents for financial compliance: Lessons from Stripe →
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “TRACE: Turn-level Reward Assignment via Credit Estimation fo” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Thysrael/Horizon →
“Fuzzy title match (0.92): “TRACE: Turn-level Reward Assignment via Credit Estimation fo” ≈ “Thysrael/Horizon””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “TRACE: Turn-level Reward Assignment via Credit Estimation fo” ≈ “Unity-Technologies/ml-agents””
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Leitian Tao →
“TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents”