arXiv AI
RELACE: retrospective likelihood-based action credit estimation for long-horizon language agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Aug 10
Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents
arXiv:2608. 06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions.
Hugging Face Trending Papers
Aug 17
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...
arXiv AI
Aug 18
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
arXiv AI
Sep 30
Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
arXiv:2609.36178v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment o...
arXiv AI
Sep 1
Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization
The paper introduces TASPO, a method that transforms privileged information (PI) into outcome‑grounded action credit for language‑model agents. TASPO constructs decision‑applicable PI from verified successful experience, aggregates PI‑induced likelihood shifts at the executable‑action level, and converts relative action support into positive, bounded, mean‑preserving weights on the original trajectory advantage. Experiments on three agentic benchmarks show TASPO improves over GRPO by 10.6% and generalizes better to unseen tasks, while reducing supervision mismatch and stabilizing policy optimization.
arXiv AI
Jul 28
Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks
arXiv:2607. 22724v1 Announce Type: cross Abstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or steps within a group.