Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents

arXiv:2609.34296 · cs.CL, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• GRPO — 52× in this paper · explained in 6 episodes
• Benchmark — 24× in this paper · explained in 2 episodes
• Credit assignment — 20× in this paper · explained in 3 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Ablation — 9× in this paper · explained in 1 episode
• LLM judge — 6× in this paper · explained in 2 episodes
• RAG — 6× in this paper · explained in 3 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• In International Conference on Learning Representations — 4× in this paper
• Verification — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Agentic Search — 2× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• In International Conference on Machine Learning — 1× in this paper
• Information Gain — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• HealthBench: Evaluating Large Language Models Towards Improved Human Health
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
• WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
• ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
• Deep Research as Rubric for Reinforcement Learning
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks
• Hindsight Credit Assignment for Long-Horizon LLM Agents
• Kimi K3: Open Frontier Intelligence
• Tongyi DeepResearch Technical Report
• QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• Qwen3 Technical Report
• GLM-5: from Vibe Coding to Agentic Engineering
• DEEPRUBRIC: Evidence-Tree Rubric Supervision for Efficient Reinforcement Learning of Deep Research Agents
• LOTAPO: Leave-One-Turn Attribution for Self-Generated Process Rewards in Multi-Turn Search Reasoning

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