For terminal-goal tasks with deterministic transitions, the authors use Bayes' rule to express hindsight credit as the ratio of success probabilities at consecutive states.
Graph-based hindsight improves credit assignment for long-horizon language agents
GraphHCA derives step-level training signals from pooled rollout graphs without a critic, hindsight model, or additional forward pass.
Top University
Haodong Zhu · Yangyang Ren · Changbai Li · Sheng Xu · Linlin Yang · haiguang liu · +1 more
Beihang University · Zhongguancun Academy · Communication University of China · Hangzhou Innovation Institute of Beihang University
Research Digest··2 min read
Zhu et al.
Why this paper
From Beihang University and 3 others · Part of Credit Assignment in Agentic RL, now 30 papers
In one line
GraphHCA assigns step-level credit in long-horizon LLM agent tasks using closed-form hindsight ratios, achieving state-of-the-art results.
What we could check
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