REVIEW 16 cited by
TextWorld: A Learning Environment for Text-based Games
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
Forward citations
Cited by 16 Pith papers
-
WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games
WebGameBench is a new benchmark that evaluates coding agents on building browser-native games from frozen specifications, with runtime browser evaluation showing best agents reach 76.9% usable rate but only 20.2% exce...
-
WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games
WebGameBench is a benchmark that evaluates coding agents by having them generate browser-native games from specifications, then running those games in a real browser to assign EXCELLENT, USABLE, or UNUSABLE labels, wi...
-
Language Models as Knowledge Bases?
BERT stores relational knowledge extractable via cloze queries without fine-tuning and matches supervised baselines on open-domain QA tasks.
-
Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
Modeling agent trajectories as action-centric probabilistic graphs lets a GNN warn LLM agents of likely step-level errors before execution, improving pass ratio ~14.7% across four benchmarks.
-
CL-bench Life: Can Language Models Learn from Real-Life Context?
CL-bench Life shows frontier language models achieve only 13.8% average success on real-life context tasks, with the best model at 19.3%.
-
LACE: Lattice Attention for Cross-thread Exploration
LACE enables parallel reasoning paths in LLMs to communicate via lattice attention and error-correct using synthetic training data, improving accuracy by over 7 points over standard parallel search.
-
Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
MISE proves that hindsight self-evaluation rewards equal minimizing mutual information plus KL divergence to a proxy policy, and experiments show 7B LLMs reaching GPT-4o-level results on validation tasks.
-
Agent Learning via Early Experience
Training agents to predict consequences of their own alternative actions (implicit world modeling) or to reflect on why expert actions were better (self-reflection) consistently improves task success, OOD generalizati...
-
CoEx -- Co-evolving World-model and Exploration
CoEx co-evolves an LLM agent's subgoal planning with an explicit, neurosymbolic belief state that is updated after each subgoal attempt, leading to improved success rates on ALFWorld, PDDL, and Jericho compared to sev...
-
Structured Feedback Improves Repair in an LLM Agent Loop
Verifier feedback that includes the failed location, observed value, and valid alternatives substantially improves LLM repair success in TextWorld; most of the gain comes from the alternatives rather than from JSON fo...
-
Multi-Agent Transactive Memory
MATM is a retrieval framework that lets populations of LLM agents share and reuse task trajectories to improve performance on interactive tasks without joint training.
-
LACE: Lattice Attention for Cross-thread Exploration
LACE enables concurrent reasoning paths in LLMs to interact via lattice attention and a synthetic training pipeline, raising accuracy more than 7 points over independent parallel search.
-
LACE: Lattice Attention for Cross-thread Exploration
LACE adds lattice attention to let parallel LLM reasoning threads interact and correct errors, raising accuracy over 7 points versus standard independent sampling.
-
Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments
Introduces GRIT, LTMI, and a hierarchical attention framework claiming performance gains on image captioning, visual dialog, and ALFRED instruction following.
-
Learning to Reason for Factuality
An online RL reward balancing factual precision, detail, and relevance is reported to reduce reasoning-LLM hallucination by 23.1 points, but the attached manuscript is an unrelated paper.
-
Why Build an Assistant in Minecraft?
A rationale is presented for developing an assistant in Minecraft to advance natural language understanding and dialogue learning.
Discussion (0). Sign in to comment.