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Self-Credit is an agentic reinforcement learning method that transforms sparse outcome rewards into dense supervision signals through self-generated credit assignment and intra-group rescaling. The approach involves the policy model summarizing key evidence with citations, refining these citations by comparing actions across successful and failed rollouts, and reweighting advantages based on the rescaled credit. Experiments on BrowseComp-Plus, GAIA, and xBench-DeepSearch show significant performance gains over baseline models and other agentic RL methods.
Dr.Credit introduces a rubric‑grounded credit assignment method that evaluates intermediate tool turns in deep research agents by comparing the information returned to the history of accepted support for each rubric. Unlike traditional approaches that rely on ground‑truth answers, Dr.Credit uses task requirements as a shared reference, distinguishing new support from previously seen evidence and recognizing partial rubric fulfillment. Experiments on four benchmarks show that Dr.Credit outperforms open deep research baselines across all primary metrics, achieving performance competitive with proprietary models while enabling more efficient evidence acquisition and higher‑quality reports under limited turn budgets.
arXiv:2608. 05987v1 Announce Type: new Abstract: Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes in long-horizon, multi-turn agentic tasks.
The paper introduces Grounded Credit Assignment (GCA) for reinforcement learning in multi‑turn search agents. GCA separates credit for evidence retrieval and answer correctness, correcting the bias in GRPO that favors evidence‑retrieving rollouts only proportionally to accuracy gains. Applying GCA to Search‑R1 boosts average EM by 5.7–6.3 points and increases the proportion of evidence‑supported correct answers from 76.9% to 93.4%.
arXiv:2608. 05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer.
arXiv:2607. 03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effectively leverage failed trajectories: full retries incur high interaction costs, while experience retrieval tends to dilute critical experience signals.