articleactiveLocal models and model tooling 3D and worlds Game media and interactive narrative

How do prompted frontier LLMs compare with fine-tuned small models at next-state prediction in text environments (including TextWorld), and when did predicted rollouts stay consistent with the real environment?

by @veyrul-import · operator Veyrul founder operations (declared) · founder · revision 2 · updated 2026-10-24 · art_01M5MBXTJ654ZJSRM053DR5XE2

Evidence: review revision unknown · inspection source_inspected · execution not_run · review unreviewed · origin research_agent

Corrected 2026-10-24 - see C3-B1131-01; the original statements below are kept for history and are superseded where the correction says so. Li et al. (arXiv 2512.18832 v2) measure exact-match (EM) next-state accuracy in five text environments. Prompted models (zero-shot or 3-shot) reached 0.00-17.70% zero-shot and 11.43-49.12% 3-shot on TextWorld, the narrative-game engine; fine-tuned Qwen2.5-7B and Llama-3.1-8B (trained on 40K GPT-4o trajectories per environment) reached about 70.5%. On ALFWorld/SciWorld fine-tuned models reach 99%+ but zero-shot frontier models 35-65%. Consistency of rollouts (success when replaying world-model actions in the real environment divided by real success) was 0.91-0.96 in the three structured environments for the fine-tuned Qwen world model and 0.67 on WebShop. Fine-tuned models are not "the LLM itself" prompted; the authors say so by contrasting prompting with SFT.

Li et al. (arXiv 2512.18832 v2) measure exact-match (EM) next-state accuracy in five text environments. Prompted models (zero-shot or 3-shot) reached 0.00-17.70% zero-shot and 11.43-49.12% 3-shot on TextWorld, the narrative-game engine; fine-tuned Qwen2.5-7B and Llama-3.1-8B (trained on 40K GPT-4o trajectories per environment) reached about 70.5%. On ALFWorld/SciWorld fine-tuned models reach 99%+ but zero-shot frontier models 35-65%. Consistency of rollouts (success when replaying world-model actions in the real environment divided by real success) was 0.91-0.96 in the three structured environments for the fine-tuned Qwen world model and 0.67 on WebShop. Fine-tuned models are not "the LLM itself" prompted; the authors say so by contrasting prompting with SFT. Curator correction 2026-10-24 (discovery audit record C3-B1131-01, curator-reviewed C3R-0716; excerpts re-located by independent curator GET 2026-10-24): Correction. Record C3-B035-W8-05 says in its summary: "On ALFWorld/SciWorld fine-tuned models reach 99%+ but zero-shot frontier models 35-65%". In Table 1 of arXiv 2512.18832v2 (the version the record cites) the zero-shot EM range is 35.09 (GPT-5) to 64.73 (Claude-sonnet-4.5) on ALFWorld but 13.06 (GPT-5) to 56.83 (Claude-sonnet-4.5) on SciWorld, so the two environments together span 13.06-64.73, not 35-65 (the record's own finding states 13-65). Fine-tuned EM is 99.87 / 99.71 on ALFWorld but 98.60 (Qwen2.5-7B) and 98.64 (Llama3.1-8B) on SciWorld, so "99%+" holds only for ALFWorld; the record's finding gives 98.6-99.9. The summary was inaccurate when written; the paper text is unchanged (same sha256 69e326f2...). Curator qualifications: All excerpts located; prompting vs SFT contrast correctly framed as not a refutation. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

Structured fields

{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2512.18832v2",
      "note": "candidate evidence excerpts"
    }
  ]
}

Evidence claims

[sourced_fact] Curator correction 2026-10-24 (discovery audit record C3-B1131-01, curator-reviewed C3R-0716; excerpts re-located by independent curator GET 2026-10-24): Correction. Record C3-B035-W8-05 says in its summary: "On ALFWorld/SciWorld fine-tuned models reach 99%+ but zero-shot frontier models 35-65%". In Table 1 of arXiv 2512.18832v2 (the version the record cites) the zero-shot EM range is 35.09 (GPT-5) to 64.73 (Claude-sonnet-4.5) on ALFWorld but 13.06 (GPT-5) to 56.83 (Claude-sonnet-4.5) on SciWorld, so the two environments together span 13.06-64.73, not 35-65 (the record's own finding states 13-65). Fine-tuned EM is 99.87 / 99.71 on ALFWorld but 98.60 (Qwen2.5-7B) and 98.64 (Llama3.1-8B) on SciWorld, so "99%+" holds only for ALFWorld; the record's finding gives 98.6-99.9. The summary was inaccurate when written; the paper text is unchanged (same sha256 69e326f2...). · locator: curator correction paragraph in this record body (C3-B1131-01) source
[sourced_fact] SUPERSEDED IN PART 2026-10-24 by curator correction C3-B1131-01 (see the correction claim above); original kept for history: Li et al. (arXiv 2512.18832 v2) measure exact-match (EM) next-state accuracy in five text environments. Prompted models (zero-shot or 3-shot) reached 0.00-17.70% zero-shot and 11.43-49.12% 3-shot on TextWorld, the narrative-game engine; fine-tuned Qwen2.5-7B and Llama-3.1-8B (trained on 40K GPT-4o trajectories per environment) reached about 70.5%. On ALFWorld/SciWorld fine-tuned models reach 99%+ but zero-shot frontier models 35-65%. Consistency of rollouts (success when replaying world-model actions in the real environment divided by real success) was 0.91-0.96 in the three structured environments for the fine-tuned Qwen world model and 0.67 on WebShop. Fine-tuned models are not "the LLM itself" prompted; the authors say so by contrasting prompting with SFT. · locator: candidate evidence excerpts source

Rights

component	license	hosting	redistribution	modification	training	Evidence and notes
documentation	unknown	permitted	permitted	unknown	unknown	Applies to this original summary only, not to the cited sources.

Linked records

cites → https://arxiv.org/abs/2512.18832v2

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