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ARXIV:2605.23652 · GAME AI · SUBMITTED 25 MAY · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.23652GAME AISUBMITTED 25 MAY · 20:33 UTCFRESHNESS STALEYoosung Hong · arXiv
A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency.
Opportunity summary
Pain A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency. Life simulation games require hundreds to thousands of non-player characters (NPCs) that behave consistently with distinct personalities…
On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster inference than an LLM-as-policy baseline. Life simulation games require…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x…
Game AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency.
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Paper Pack
10.48550/arXiv.2605.23652A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency.
Abstract
On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster inference than an LLM-as-policy baseline. Life simulation games require hundreds to thousands of non-player characters (NPCs) that behave consistently with distinct personalities while remaining controllable through designer-authored natural language. Existing methods fail on constraints like persona consistency, controllability, or real-time inference. We introduce pcsp (Persona Conditioned Shared Policy), a single reinforcement learning policy conditioned on frozen LLM embeddings of free-form persona descriptions. pcsp combines once-per-NPC persona encoding, low-rank persona projection, neural persona conditioning, and a PPO + InfoNCE consistency + KL diversity training objective. Across three experimental settings, ablations show that the InfoNCE trajectory-consistency objective is load bearing: removing it collapses zero-shot persona identification to chance. External validation on Melting Pot 2.4.0 substrates confirms that our method produces persona-conditioned behavioral divergence in multi-agent strategic environments. We distinguish two senses of held-out evaluation: compositional zero-shot and vocabulary-expansion held-out. Finally, a UE5 deployment reproduces the in-engine persona-conditioning ablation at 64 agents with a low failure rate, showing that the sub-frame inference profile survives in a commercial game engine. These results prove that shared RL policies can support scalable, real-time, persona-conditioned NPC control.
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Proof status
unverified0 refs; 4 sources; 67% coverage.
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Dimensions overall score 7.0
PROBLEM
A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency. Life simulation games require hundreds to thousands of non-player characters (NPCs) that behave consistently with distinct personalities while remaining cont...
METHOD
On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster inference than an LLM-as-policy baseline. Life simulation games require hundred...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster infe...
WHY NOW
Game AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency. Life simulation games require hundreds to thousands of non-player characters (NPCs) that behave consistently with distinct personalities while remaining controllable through designer-authored natural language.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster inference than an LLM-as-policy baseline. Life simulation games require hundreds to thousands of non-player characters (NPCs) that behave consistently with distinct personalities while remaining controllable through designer-authored natural language.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.73 semantic-behavioral alignment, and 22x faster inference than an LLM-as-policy baseline. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Game AI moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A single reinforcement learning policy for scalable game agents that can adopt diverse personas and maintain consistency.
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Game AI
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Commercial read
7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
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stale
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passport absent
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Technical feasibility
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Evidence
0 references, 4 sources, 67% evidence coverage.
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