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Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond

Hao Sun

TL;DR

This paper surveys reinforcement learning concepts in the context of large language models, reframing RLHF as online inverse reinforcement learning that leverages known autoregressive dynamics to sidestep offline-RL pitfalls. It argues that RLHF outperforms supervised fine-tuning by mitigating compounding error through imitation-based optimization and reward modeling, and it posits online IRL as a principled framework for alignment. The authors introduce Prompt-OIRL, an offline IRL approach to query-dependent prompt evaluation and optimization, highlighting cost efficiency and applicability to prompting tasks. Collectively, the work provides a structured RL-centered perspective on RLHF and prompts, identifying key challenges and proposing concrete directions for improving alignment and prompting in LLMs.

Abstract

Recent advancements in Large Language Models (LLMs) have garnered wide attention and led to successful products such as ChatGPT and GPT-4. Their proficiency in adhering to instructions and delivering harmless, helpful, and honest (3H) responses can largely be attributed to the technique of Reinforcement Learning from Human Feedback (RLHF). In this paper, we aim to link the research in conventional RL to RL techniques used in LLM research. Demystify this technique by discussing why, when, and how RL excels. Furthermore, we explore potential future avenues that could either benefit from or contribute to RLHF research. Highlighted Takeaways: 1. RLHF is Online Inverse RL with Offline Demonstration Data. 2. RLHF $>$ SFT because Imitation Learning (and Inverse RL) $>$ Behavior Cloning (BC) by alleviating the problem of compounding error. 3. The RM step in RLHF generates a proxy of the expensive human feedback, such an insight can be generalized to other LLM tasks such as prompting evaluation and optimization where feedback is also expensive. 4. The policy learning in RLHF is more challenging than conventional problems studied in IRL due to their high action dimensionality and feedback sparsity. 5. The main superiority of PPO over off-policy value-based methods is its stability gained from (almost) on-policy data and conservative policy updates.

Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond

TL;DR

This paper surveys reinforcement learning concepts in the context of large language models, reframing RLHF as online inverse reinforcement learning that leverages known autoregressive dynamics to sidestep offline-RL pitfalls. It argues that RLHF outperforms supervised fine-tuning by mitigating compounding error through imitation-based optimization and reward modeling, and it posits online IRL as a principled framework for alignment. The authors introduce Prompt-OIRL, an offline IRL approach to query-dependent prompt evaluation and optimization, highlighting cost efficiency and applicability to prompting tasks. Collectively, the work provides a structured RL-centered perspective on RLHF and prompts, identifying key challenges and proposing concrete directions for improving alignment and prompting in LLMs.

Abstract

Recent advancements in Large Language Models (LLMs) have garnered wide attention and led to successful products such as ChatGPT and GPT-4. Their proficiency in adhering to instructions and delivering harmless, helpful, and honest (3H) responses can largely be attributed to the technique of Reinforcement Learning from Human Feedback (RLHF). In this paper, we aim to link the research in conventional RL to RL techniques used in LLM research. Demystify this technique by discussing why, when, and how RL excels. Furthermore, we explore potential future avenues that could either benefit from or contribute to RLHF research. Highlighted Takeaways: 1. RLHF is Online Inverse RL with Offline Demonstration Data. 2. RLHF SFT because Imitation Learning (and Inverse RL) Behavior Cloning (BC) by alleviating the problem of compounding error. 3. The RM step in RLHF generates a proxy of the expensive human feedback, such an insight can be generalized to other LLM tasks such as prompting evaluation and optimization where feedback is also expensive. 4. The policy learning in RLHF is more challenging than conventional problems studied in IRL due to their high action dimensionality and feedback sparsity. 5. The main superiority of PPO over off-policy value-based methods is its stability gained from (almost) on-policy data and conservative policy updates.
Paper Structure (24 sections, 2 theorems, 5 equations, 10 figures, 1 table)

This paper contains 24 sections, 2 theorems, 5 equations, 10 figures, 1 table.

Table of Contents

  1. A Crash Introduction to RL: Online RL, Offline RL, and Inverse RL
  2. Essential Concepts
  3. Technical Formumation
  4. Markov Decision Processes
  5. Online RL
  6. Offline RL
  7. Imitation Learning
  8. Another Motivation of IL: Reward Design is Hard
  9. A Method for Reward Engineering
  10. Inverse Reinforcement Learning
  11. Learning from Demonstrations
  12. Comparison Between Different Settings
  13. RLHF: Solving the Problem of Offline RL with Online Inverse RL
  14. LLM Alignment from Human Feedback
  15. Aligning with Human Preference: the Online Nature and Offline Practice
  16. ...and 9 more sections

Key Result

Theorem 1.1

If $\pi$ is trained via empirical risk minimization on $s_t\sim p_{\pi_\beta}(\tau)$ and optimal labels $a_t^*$, and attains generalization error $\epsilon$ on $s_t \sim p_{\pi_\beta}(\tau)$, then $\ell(\pi)\le C+T^2 \epsilon$ is the best possible bound on the expected error of the learned policy.

Figures (10)

  • Figure 1: A pictorial illustration of RL: an agent interacts with the environment and learns from trial and error.
  • Figure 2: In Offline RL, a behavior policy interacts with the environment and generates a decision dataset. Then such a decision dataset is used to learn a policy without access to the environment (offline).
  • Figure 3: In Imitation Learning (IL), the agent learns from feedback from the decision dataset, but the observations are from a real dynamics model.
  • Figure 4: Inverse Reinforcement Learning (IRL) solves the IL tasks in two steps: (1). reward modeling that distills the knowledge of underlying learning objectives that the behavior policy seems to optimize from the offline decision demonstration dataset. (2). combining such a learned reward model and the accessible dynamics model, everything needed for an online RL algorithm is right there.
  • Figure 5: (From ouyang2022training) There are 3 steps to align LLMs to human preference. Step 1: supervised fine-tuning of pre-trained LLM to follow instructions (generated by human demonstration data). Step 2: sample multiple responses for every query, and rank those responses according to human preference. Then a reward model can be learned to mimic the human preference. Step 3: Optimize the language model through RL to maximize the feedback from the reward model
  • ...and 5 more figures

Theorems & Definitions (5)

  • Theorem 1.1: Behavior Clone Error Bound. ross2011reduction
  • Remark 1.2: Compounding Error.
  • Remark 1.3: Behavior Clone
  • Theorem 1.4: DAgger Error Bound, ross2011reduction
  • Remark 1.5