Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 72 tok/s
Gemini 2.5 Pro 45 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 211 tok/s Pro
GPT OSS 120B 442 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Q-Probe: A Lightweight Approach to Reward Maximization for Language Models (2402.14688v2)

Published 22 Feb 2024 in cs.LG

Abstract: We present an approach called Q-probing to adapt a pre-trained LLM to maximize a task-specific reward function. At a high level, Q-probing sits between heavier approaches such as finetuning and lighter approaches such as few shot prompting, but can also be combined with either. The idea is to learn a simple linear function on a model's embedding space that can be used to reweight candidate completions. We theoretically show that this sampling procedure is equivalent to a KL-constrained maximization of the Q-probe as the number of samples increases. To train the Q-probes we consider either reward modeling or a class of novel direct policy learning objectives based on importance weighted policy gradients. With this technique, we see gains in domains with ground-truth rewards (code generation) as well as implicit rewards defined by preference data, even outperforming finetuning in data-limited regimes. Moreover, a Q-probe can be trained on top of an API since it only assumes access to sampling and embeddings. Code: https://github.com/likenneth/q_probe .

Definition Search Book Streamline Icon: https://streamlinehq.com
References (53)
  1. Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models. arXiv preprint arXiv:2311.18232, 2023.
  2. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2016.
  3. Thinking fast and slow with deep learning and tree search. Advances in neural information processing systems, 30, 2017.
  4. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.
  5. Llemma: An open language model for mathematics. arXiv preprint arXiv:2310.10631, 2023.
  6. Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, pages 1–12, 2016.
  7. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39:324, 1952. URL https://api.semanticscholar.org/CorpusID:125209808.
  8. Learning to generate better than your llm. arXiv preprint arXiv:2306.11816, 2023.
  9. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.
  10. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017.
  11. Raft: Reward ranked finetuning for generative foundation model alignment. arXiv preprint arXiv:2304.06767, 2023.
  12. Understanding dataset difficulty with v𝑣vitalic_v-usable information. In International Conference on Machine Learning, pages 5988–6008. PMLR, 2022.
  13. Human-centered loss functions (halos). Technical report, Contextual AI, 2023.
  14. Turbotransformers: an efficient gpu serving system for transformer models. In Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 389–402, 2021.
  15. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858, 2022.
  16. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5(9), 2004.
  17. Reinforced self-training (rest) for language modeling. arXiv preprint arXiv:2308.08998, 2023.
  18. A structural probe for finding syntax in word representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4129–4138, 2019.
  19. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751, 2019.
  20. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  21. Editing models with task arithmetic. arXiv preprint arXiv:2212.04089, 2022.
  22. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  23. Openassistant conversations–democratizing large language model alignment. arXiv preprint arXiv:2304.07327, 2023.
  24. Emergent world representations: Exploring a sequence model trained on a synthetic task. arXiv preprint arXiv:2210.13382, 2022.
  25. Starcoder: may the source be with you! arXiv preprint arXiv:2305.06161, 2023.
  26. Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. arXiv preprint arXiv:2104.08786, 2021.
  27. Rethinking the role of demonstrations: What makes in-context learning work? arXiv preprint arXiv:2202.12837, 2022.
  28. Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation. arXiv preprint arXiv:2305.16938, 2023.
  29. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
  30. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022.
  31. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444, 2017.
  32. Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290, 2023.
  33. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241, 2022.
  34. A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics, 8:842–866, 2021.
  35. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950, 2023.
  36. In-context impersonation reveals large language models’ strengths and biases. arXiv preprint arXiv:2305.14930, 2023.
  37. Self-critiquing models for assisting human evaluators. arXiv preprint arXiv:2206.05802, 2022.
  38. Trust region policy optimization. In International conference on machine learning, pages 1889–1897. PMLR, 2015.
  39. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
  40. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2023.
  41. Beyond human data: Scaling self-training for problem-solving with language models. arXiv preprint arXiv:2312.06585, 2023.
  42. Offline rl for natural language generation with implicit language q learning. arXiv preprint arXiv:2206.11871, 2022.
  43. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  44. Unsupervised discovery of interpretable directions in the gan latent space. In International conference on machine learning, pages 9786–9796. PMLR, 2020.
  45. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  46. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733–3742, 2018.
  47. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.
  48. Orca: A distributed serving system for {{\{{Transformer-Based}}\}} generative models. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), pages 521–538, 2022.
  49. Scaling relationship on learning mathematical reasoning with large language models. arXiv preprint arXiv:2308.01825, 2023.
  50. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199, 2021.
  51. Star: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems, 35:15476–15488, 2022.
  52. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406, 2023a.
  53. Sotopia: Interactive evaluation for social intelligence in language agents. arXiv preprint arXiv:2310.11667, 2023b.
Citations (7)
View on Semantic Scholar

Summary

  • The paper introduces Q-Probing, a method that leverages a simple linear function on the embedding space to reweight model completions for improved task-specific rewards.
  • It employs rejection sampling and importance weighted policy gradients, providing a compute-efficient alternative to traditional finetuning.
  • Empirical evaluations show that Q-Probe outperforms finetuning and few-shot prompting in tasks like code generation and preference-based adaptation in data-limited scenarios.

Q-Probe: Enhancing LLM Performance with Lightweight Probing

Introduction

LLMs have demonstrated impressive capabilities in various natural language processing tasks. However, adapting these models to specific tasks often requires further adjustment to better align their output with specific goals or reward functions. Traditional methods for this adaptation include finetuning and prompting, each with its costs and benefits. We introduce an alternative approach named Q-probing, designed to optimize a pre-trained LLM for a task-specific reward function efficiently. Q-probing operates by learning a simple linear function on the model's embedding space to reweight candidate completions, striking a middle ground between less intensive methods like few-shot prompting and more comprehensive ones like finetuning.

Theoretical Framework

Q-probing capitalizes on the insight that the requisite knowledge for many tasks already exists within the LLM due to its pre-training, and task-specific adaptation is more about extracting relevant information. It employs a method that can be viewed as a form of rejection sampling, where candidate completions are drawn from the LLM, evaluated through the probe, and reweighted according to their estimated utility. Theoretically, this method approximates a KL-constrained optimization process, aiming to maximize the task-specific reward as the number of completions (samples) increases.

Methodology

Q-probing's implementation involves training a Q-probe, a linear model, using either a reward modeling approach or direct policy learning via importance weighted policy gradients. This allows the adaptation to different feedback types (ground-truth rewards or user preferences) and interaction levels (offline datasets or online reward access). Notably, Q-probe training requires minimal computational resources compared to traditional finetuning methods and can easily be applied on top of LLMs accessed via API, broadening its applicability.

Empirical Evaluation

Q-probing demonstrates superior performance in several domains, notably in code generation tasks where it outperforms both traditional finetuning methods and few-shot prompting, especially in data-limited scenarios. For tasks with implicit rewards defined by preference data, Q-probe again shows an ability to surpass baseline methods, including finetuning strategies, validating its effectiveness across different types of feedback.

Implications and Future Directions

The Q-probing method presents a compelling alternative for adapting LLMs to specific tasks, especially when computational resources are limited or when working with API-based models. Its ability to effectively utilize the model's pre-trained capabilities with minimal additional training represents a significant efficiency gain. Future research could explore the potential of Q-probes in iterative finetuning processes or in conjunction with other adaptation strategies to further enhance performance. Additionally, investigating the transferability of probes across tasks could unveil new insights into LLMs' representational properties and the nature of task-specific knowledge within these models.

Conclusion

Q-probing offers a balanced and efficient approach to optimizing LLMs for specific tasks, bridging the gap between lightweight prompting methods and computationally expensive finetuning. By leveraging the model's inherent capabilities with a targeted probing strategy, it sets the stage for more adaptable, efficient, and effective use of LLMs across a wide range of applications.

This summary highlights the key points of the Q-probing approach, focusing on its theoretical foundation, implementation details, evaluation results, and implications for future research within the field of generative AI and LLMs.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 5 posts and received 208 likes.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up to View