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Reported in 65 papers on Zotpaper. The latest, each as the paper states it:
58 results for “ALFWorld” · page 1 of 3
Research · Agents
CORD: Conflict-Aware Optimization for Joint RL and Distillation in Agents
CORD selectively removes opposing gradient components while preserving a protected objective, boosting task success on ALFWorld and WebShop across several training recipes.
ALFWorld: 77.9% pooled success rate (as reported)
The Chinese University of Hong Kong, Tencent, Peking University
Research · Agents
SAPD: Learning from Agent Experience without Environment Interaction
SAPD decomposes recorded multi-turn histories into decision prefixes and uses relative step advantages to stabilize policy distillation, improving results on ALFWorld, WebShop and Search-based QA.
ALFWorld: 69.3% success rate (as reported)
Meituan, University of Chinese Academy of Sciences
Research · Agents
Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents
The nonparametric framework improves LLM agent success on WebArena-Lite and ALFWorld by separating immediate guidance from cross-episode trust.
ALFWorld: 88.1% success rate (as reported)
Renmin University of China, Tencent
Research · Reinforcement learning
BLPO: Beyond-Local Policy Optimization for Long-Horizon LLM Agents
Across two simulated environments and two model sizes, the authors report higher task success when training reuses evidence about useful actions across tasks and training updates.
ALFWorld: 97.14% In-Success (as reported)
Beihang University, Alibaba Group
Research · Agents
RLiD: Reinforcement Learning in the Decision Plane for Language Agents and LLMs
On a household-task benchmark, training a model to choose directly among labelled actions reached 99.3% success in 12.7 GPU-hours, where token-level reinforcement learning reached 86.6% in 17.3.
ALFWorld: 99.3% success (as reported)
University of Illinois Urbana-Champaign, National University of Singapore, Zhejiang University
Research · Agents
Verify--Backtrack Loop: Structured Precondition Recovery for Long-Horizon Language Agents
A training-free goal-stack controller lifts task success by up to 22 percentage points over ReAct across three benchmarks.
ALFWorld: 87.31% task success rate (as reported)
City University of Hong Kong, Huawei Noah's Ark Lab
Research · Evaluation & benchmarks
Retrieved but not ranked: surface-form bias in structural retrieval, from mathematics to agent trajectories
Across competition mathematics and embodied-agent trajectories, embedding models retrieved plausible candidates but often ranked lexical overlap ahead of structural similarity.
MathNet-Retrieve: 0% Hit@1 (as reported)
Independent, MIT CSAIL, Kellis Lab
Research · Agents
TalentRL: Strategy-Guided Reinforcement Learning for Hierarchical Skill Routing
A hierarchical reinforcement learning approach separates strategy selection from skill execution to improve routing credit assignment.
ALFWorld: 99.3% success rate (as reported)
Microsoft AI
Research · Reinforcement learning
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
HP-RLVR uses a trajectory’s realized future and verifier feedback to weight token updates without group sampling, reference answers or a learned critic.
ALFWorld: 2.3 x training speedup (as reported)
South China University of Technology, Tsinghua University, The Hong Kong University of Science and Technology
Research · Reinforcement learning
Contextual Credit Policy Optimization
CCPO uses context-weighted trajectory comparisons and short-horizon progress to identify which actions contributed to eventual success.
ALFWorld: 21.6 pts success rate gain over GRPO (as reported)
Stanford University, Texas A&M University, Yale University
Research · Agents
VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents
VETTA learns both credit types via separate value heads on a lightweight shared critic, improving success rates on two agent benchmarks.
ALFWorld: 91.9% success rate (as reported)
University of Science and Technology of China, Zhongguancun Academy, Shandong University
Research · Language models
Branching On-Policy Distillation
Chen et al. show that resampling continuations at informative decision points broadens teacher supervision and outperforms independent rollouts with fewer student tokens.
ALFWorld: 19.1% student-generated token cost saving vs seven independent (as reported)
Tsinghua University, SenseTime Research, Peking University
Research · Agents
Self-Evolving Agents through Privileged Retrospective Distillation
Retrospective Self-Distillation converts completed trajectories, including failures, into training signals without ground-truth solutions or verifier rewards for policy updates.
ALFWorld: 78.4% success rate (SR) (as reported)
Meta AI
Research · Reinforcement learning
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
ProAS uses a reference graph to mask invalid actions and penalize early deviations, boosting success and convergence.
ALFWorld: 16.7 pts success rate improvement over GRPO (as reported)
Zhejiang University, Westlake University, Westlake Institute for Advanced Study
Research · Reinforcement learning
TrajEvolve: Trajectory Evolution for Reinforcement Learning with Hindsight Credit Assignment
TrajEvolve revises unnecessary steps in historical trajectories and uses the resulting paths alongside fresh policy rollouts during training.
ALFWorld: 95.3% success rate (as reported)
City University of Hong Kong, Tencent, Nanjing University
Research · Agents
MemScope: Evolving Agent Memory Architectures through Localized Code Repair
MemScope uses a tool-using Patch Agent to inspect raw traces, runtime memory, and code to identify and apply bounded, in-place edits, outperforming summary-based feedback on multiple benchmarks.
ALFWorld: 71.64% success (as reported)
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences
Research · Agents
Beyond the Teacher: Understanding and Preserving Distillation Overshoot in Agentic On-Policy Distillation
Zhong et al. show that on-policy distillation repairs weak student decisions early but erodes successful ones later, and propose SPOD to protect them.
ALFWorld: 88.9% success (as reported)
University of Southern California, University of Florida, Emory University
Research · Agents
State-OPD: State-Grounded On-Policy Distillation for Multi-Turn Agents
State-OPD separates executed interaction history from student reasoning, improving teacher supervision and broadening the actions sampled during training.
ALFWorld: 36.01% success rate (as reported)
Alibaba Group, Tsinghua University, Shanghai Jiao Tong University
Research · Agents
When Faster Decisions Do Not Make Better Agents: An Empirical Study of Jev in Agent Workflows
Across 4,500 executions, cheaper action selection produced workflow-dependent savings and sometimes reduced successful task delivery.
ALFWorld: 3.96 mUSD cost per delivered success (as reported)
Nanjing University, Technical University of Munich
Research · Language models
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision
DTDD uses segment-level divergence to switch from on-policy distillation to teacher-generated recovery, improving stability and sample efficiency across reasoning and agentic tasks.
ALFWorld: 81.32% average success (as reported)
Google, University of Washington, Boston University