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29 results for “ALFWorld” · page 2 of 2
Research · Agents
ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience
ASCENT enables online test-time training of LLM agents by self-distilling successful trajectories into persistent weight updates without external supervision.
ALFWorld: 69.5% success rate (as reported)
University of New South Wales (UNSW Sydney)
Research · Agents
Learning from Interaction Experience via Localized Context Distillation
The authors propose a method that identifies and revises only critical spans in a student's trajectory, outperforming both off-policy and on-policy context distillation across math, code, and agent tasks.
ALFWorld: 33.08% task success rate (as reported)
Tencent, The Chinese University of Hong Kong, Zhejiang University
Research · Agents
EvoBelief: Falsifiable World Models for Self-Improving LLM Agents
EvoBelief introduces a lifecycle of hypothesis formation, validation, and revision that improves task performance across static and changing environments.
ALFWorld: 66.42% success rate (as reported)
Tulane University, Amazon
Research · Agents
Plan-and-Patch: Diffusion Language Models for Agentic Planning
The Plan-and-Patch framework uses parallel unmasking for planning and infilling for repairs, outperforming autoregressive models in repair success and latency.
ALFWorld: 28.4% task success rate after reference-assisted repair (as reported)
University of Texas at Austin, Amazon
Research · Agents
Sibyl: An Efficient Small-large Model Collaboration Framework for Long-horizon Tasks
Sibyl trains a 0.6-billion-parameter model to request step-level assistance only when useful, then absorb that guidance into later decisions.
ALFWorld: 7.7% call ratio (as reported)
Hangzhou Dianzi University, Fudan University, The Hong Kong University of Science and Technology
Research · Agents
Procedural Exposure Bias in Skill-Augmented Language Model Agents
The same procedural content, shown as text versus as an image, leads to measurable differences in action preference and downstream agent success.
ALFWorld, ScienceWorld, WebShop: 44.06% macro-average success rate (as reported)
Northeastern University, Alibaba Group
AI
Framework dynamically adapts static benchmarks to target agent weaknesses, boosting performance across multiple models
1 source
Research · Reinforcement learning
From Outcomes to Steps: Reliable Success-Aware Critic Learning for Long-Horizon Language Agents
SCALE adds an explicit success probability head to actor-critic RL and uses reliability-weighted changes in that probability to credit intermediate actions, raising success rates on five agent benchmarks.
ALFWorld: 93.4% success rate (as reported)
Shanghai Jiao Tong University, Huawei Noah's Ark Lab
Research · Agents
Co-Evolving Acting and Forecasting for Language Agents
CoPE trains agents to predict several subsequent actions from each interaction history, improving five benchmark results without adding inference-time computation.
ALFWorld: 95.5% success rate (as reported)
University of Illinois Urbana-Champaign, University of California San Diego, Meta AI