mlsanityData to 7 Oct 2026

Experience-Sensitive Game Learning: A Behavioral Study of Humans and Language Agents

arXiv 2608.07490 ↗First posted 15 Jun 2026cs.HC cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from repeated interaction. We study experience-sensitive game learning: how gameplay experience changes the decision-making behavior of humans and language agents. We formulate experience-sensitive game learning as a framework for analyzing behavioral change across repeated gameplay, rather than only final score or win rate. We introduce a suite of interactive games with reusable strategic structure, together with cross-game greedy-to-global metrics and game-specific behavioral diagnostics that make experience-driven change observable from action traces. We also collect repeated-game trajectories from human players and evaluate recent self-evolving language agents in the same behavioral metric space. Our results show that human players exhibit interpretable and relatively stable shifts from locally greedy heuristics toward more global strategic decisions. In contrast, current self-evolving agents often show noisy and transient gains, suggesting that existing self-evolution methods remain limited in converting gameplay experience into durable changes in decision-making behavior.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 19 of its 32 references that are in the map. First posted on arXiv 15 Jun 2026.

Builds on

19 of 32 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning29 Sep
  2. 2025Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games4 Jun
  3. 2025DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments8 Mar
  4. 2024GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents7 Jun

Other references · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 551
  3. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 319
  4. 2018TextWorld: A Learning Environment for Text-based Gamescited by 304
  5. 2019Interactive Fiction Games: A Colossal Adventurecited by 165
  6. 2025EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecyclecited by 129
  7. 2020Keep CALM and Explore: Language Models for Action Generation in Text-based Gamescited by 108
  8. 2023CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalizationcited by 88
  9. 2025lmgame-Bench: How Good are LLMs at Playing Games?cited by 54
  10. 2025EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic Systemscited by 34
  11. 2024GameArena: Evaluating LLM Reasoning through Live Computer Gamescited by 32
  12. 2026Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updatescited by 17
3 more
  1. 2026M3-BENCH: Process-Aware Evaluation of LLM Agents' Social Behaviors in Mixed-Motive Gamescited by 8
  2. 2025Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Makingcited by 5
  3. 2026Meta-TTL: Meta-Learning Self-Improvement Policies for Language Agentscited by 2

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  1. 2025Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
  2. 2026Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?
  3. 2025Playing games with Large language models: Randomness and strategy
  4. 2026Rethinking Experience Utilization in Self-Evolving Language Model Agents
  5. 2025Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making
  6. 2026Large Language Model Agents Are Not Always Faithful Self-Evolvers
  7. 2025Assessing Adaptive World Models in Machines with Novel Games
  8. 2025The Effect of State Representation on LLM Agent Behavior in Dynamic Routing Games