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Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling (2407.02446v1)

Published 2 Jul 2024 in cs.CL and cs.AI

Abstract: RLHF-aligned LMs have shown unprecedented ability on both benchmarks and long-form text generation, yet they struggle with one foundational task: next-token prediction. As RLHF models become agent models aimed at interacting with humans, they seem to lose their world modeling -- the ability to predict what comes next in arbitrary documents, which is the foundational training objective of the Base LMs that RLHF adapts. Besides empirically demonstrating this trade-off, we propose a potential explanation: to perform coherent long-form generation, RLHF models restrict randomness via implicit blueprints. In particular, RLHF models concentrate probability on sets of anchor spans that co-occur across multiple generations for the same prompt, serving as textual scaffolding but also limiting a model's ability to generate documents that do not include these spans. We study this trade-off on the most effective current agent models, those aligned with RLHF, while exploring why this may remain a fundamental trade-off between models that act and those that predict, even as alignment techniques improve.

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References (35)
  1. Self-consuming generative models go mad. arXiv preprint arXiv:2307.01850.
  2. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862.
  3. Large language models suffer from their own output: An analysis of the self-consuming training loop. arXiv preprint arXiv:2311.16822.
  4. Mode regularized generative adversarial networks. In International Conference on Learning Representations.
  5. Recall and learn: Fine-tuning deep pretrained language models with less forgetting. arXiv preprint arXiv:2004.12651.
  6. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.
  7. On the use of arxiv as a dataset. Preprint, arXiv:1905.00075.
  8. Faith and fate: Limits of transformers on compositionality. Advances in Neural Information Processing Systems, 36.
  9. The Pile: An 800GB dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027.
  10. Aligning language models with preferences through f-divergence minimization. Preprint, arXiv:2302.08215.
  11. Olmo: Accelerating the science of language models. arXiv preprint arXiv:2402.00838.
  12. The curious case of neural text degeneration. In International Conference on Learning Representations.
  13. Eduard Hovy and Chin-Yew Lin. 1998. Automated text summarization and the summarist system. In TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998, pages 197–214.
  14. Camels in a changing climate: Enhancing lm adaptation with tulu 2. arXiv preprint arXiv:2311.10702.
  15. Sequence tutor: Conservative fine-tuning of sequence generation models with KL-control. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1645–1654. PMLR.
  16. Kazutaka Katoh and Daron M Standley. 2013. Mafft multiple sequence alignment software version 7: improvements in performance and usability. Molecular biology and evolution, 30(4):772–780.
  17. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521–3526.
  18. Openassistant conversations-democratizing large language model alignment. Advances in Neural Information Processing Systems, 36.
  19. From distributional to overton pluralism: Investigating large language model alignment. arXiv [cs.CL].
  20. Self-alignment with instruction backtranslation. In The Twelfth International Conference on Learning Representations.
  21. Avoiding data contamination in language model evaluation: Dynamic test construction with latest materials. Preprint, arXiv:2312.12343.
  22. An empirical study of catastrophic forgetting in large language models during continual fine-tuning. arXiv preprint arXiv:2308.08747.
  23. Which training methods for GANs do actually converge? In Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 3481–3490. PMLR.
  24. Future lens: Anticipating subsequent tokens from a single hidden state. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 548–560.
  25. The LAMBADA dataset: Word prediction requiring a broad discourse context. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1525–1534, Berlin, Germany. Association for Computational Linguistics.
  26. Red teaming language models with language models. Preprint, arXiv:2202.03286.
  27. Exploring the limits of transfer learning with a unified text-to-text transformer. Technical report, Google.
  28. The curse of recursion: Training on generated data makes models forget. arXiv preprint arXiv:2305.17493.
  29. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  30. Overcoming catastrophic forgetting in zero-shot cross-lingual generation. arXiv preprint arXiv:2205.12647.
  31. Mint: Evaluating llms in multi-turn interaction with tools and language feedback. In The Twelfth International Conference on Learning Representations.
  32. On the algorithmic bias of aligning large language models with rlhf: Preference collapse and matching regularization. Preprint, arXiv:2405.16455.
  33. Forget me not: Reducing catastrophic forgetting for domain adaptation in reading comprehension. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
  34. How language model hallucinations can snowball. arXiv preprint arXiv:2305.13534.
  35. LIMA: Less is more for alignment. In Thirty-seventh Conference on Neural Information Processing Systems.
Citations (4)
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Summary

  • The paper demonstrates that RLHF-adapted models excel in goal-directed tasks but suffer degraded next-token prediction performance.
  • It reveals that RLHF induces a concentration of probability mass on select outputs, reducing diversity through implicit blueprinting.
  • The study highlights a core challenge in balancing world modeling uncertainty with precise agent actions, suggesting future hybrid approaches.

Predicting vs. Acting: A Trade-off Between World Modeling and Agent Modeling

The paper "Predicting vs. Acting: A Trade-off Between World Modeling and Agent Modeling" presents an in-depth analysis of the inherent trade-offs encountered when LMs transition from being world models to agent models via Reinforcement Learning from Human Feedback (RLHF). This transition, while advantageous for the development of coherent long-form text generation, appears to compromise the core ability of LMs to predict arbitrary next tokens, which is foundational for their operation as world models.

Key Findings

Performance Trade-off

The research highlights a clear empirical observation: RLHF-aligned LMs, while excelling in goal-directed tasks, exhibit degraded performance in next-token prediction. This is substantiated by experiments showing that RLHF models consistently underperform base LMs on various perplexity metrics across multiple corpora. Even when finetuned, the adapted models fail to reclaim their original world modeling capabilities, confirming the presence of a fundamental trade-off.

Distribution Concentration

A significant finding is that RLHF models concentrate their probability mass onto a smaller set of likely text outcomes. This is evidenced by their higher alignment and overlap in generated sequences when compared to base models. Utilizing sequence alignment techniques, the authors demonstrate that RLHF models tend to generate highly similar outputs for the same prompt, often re-using long text spans termed "anchor spans." These spans serve as a form of implicit blueprint or scaffolding for the generated text, which restricts the randomness and variability typically found in base LM outputs.

Implicit Blueprinting

The paper elaborates on how RLHF models employ these anchor spans to maintain coherence in long-form generations. This blueprinting is demonstrated through the notable reuse of nn-grams and structural similarities across different generations for the same prompt. Data visualizations using Sankey diagrams illustrate the uniformity and predictability of RLHF model outputs, strongly contrasting with the diverse outputs of base LMs even under controlled diversity conditions.

Planning and Predictability

Furthermore, RLHF models exhibit a pronounced ability to "think ahead," with their internal states containing information predictive of future tokens. Linear probing experiments reveal that the hidden representations of RLHF models are more informative of subsequent tokens compared to those of base models. This quality is essential for action-oriented tasks requiring consistency and long-term planning, reinforcing the specialized capabilities of RLHF models as effective agent models.

Implications and Future Directions

Theoretical Implications

The observed trade-offs between world modeling and agent modeling imply that achieving a balance between these capabilities within a single model might be inherently challenging due to their opposing requirements. World models rely on maintaining the true uncertainty of natural language, while agent models benefit from minimizing uncertainty to ensure coherent action sequences. This dichotomy suggests a fundamental constraint in the design of LMs aimed at both predicting arbitrary text and performing goal-directed actions.

Practical Implications

Practically, the findings suggest that RLHF-adapted models, while powerful for specific agent-based applications, may not be ideal for tasks requiring broad coverage and diversity of text prediction. This has significant ramifications for the deployment of AI systems, particularly in applications where both reliability in action and adaptability in understanding are critical.

Future Developments

Exploring hybrid approaches where distinct models or components specialize in world modeling and agent modeling might offer a viable path forward. Integrating mechanisms that dynamically switch between these modes based on the task at hand could mitigate the trade-offs identified in this paper. Additionally, refining RLHF techniques to reduce the collapse of distribution while maintaining planning capabilities could further enhance the synergy between act-and-predict functions in LMs.

Conclusion

This paper provides a comprehensive analysis of the trade-offs between world modeling and agent modeling in RLHF-aligned LMs. The empirical evidence presented underscores the impacts of probability concentration, predictability, and planning abilities on the performance of these models. The paper not only elucidates the current limitations but also paves the way for future research aimed at optimizing the dual capacities of LMs to predict and to act effectively. This contributes significantly to our understanding of the evolving landscape of AI and the architectural considerations needed for the next generation of LLMs.

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