ProAS reshapes advantages for multi-turn agent reinforcement learning
On Qwen2.5 instruct models, reported peaks reach 94.3% on ALFWorld and 70.8% on WebShop.
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Stories tagged #webshop
Co-Evolving Acting and Forecasting for Language Agents
CoPE trains language agents to act and forecast future actions, improving benchmark scores and sample efficiency.
SERO: Learning from Executable Oracle Continuations for Long-Horizon Agentic Reinforcement Learning
SERO trains long-horizon agents using executable oracle continuations and reachability weighting on ALFWorld and WebShop.
Branching On-Policy Distillation
BranchOPD branches uncertain on-policy rollouts, beating vanilla OPD by 18.55 math and 13.8 ALFWorld points.
UniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
UniSkill lets one policy act and propose skill edits using contrastive feedback without extra rollouts.
ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience
ASCENT updates deployed LLM agents by self-distilling verified trajectories into LoRA weights at test time.
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
A 50,228-pair Agent Error Dataset diagnoses LLM agent failures and improves correction and post-training.
X-Tree: Tokenizing Reusable Experience for Efficient Agent Generalization
X-Tree tokenizes reusable agent skills and raises success on WebArena, ScienceWorld, and WebShop.
Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents
Just-in-Time Memory curates raw agent trajectories at read time and beats write-time memory on three benchmarks.
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
RetireOPD adds self-retiring on-policy distillation to agentic RL, boosting Qwen2.5 agent success on ALFWorld by 14-19% and WebShop by 12-19%.