Lean and Mean: Decoupled Value Policy Optimization with Global Value Guidance

↗ arXiv ↗ Hugging Face

TL;DR

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Key Takeaways

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Why does it matter?

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This paper is important for researchers interested in efficient LLM alignment. DVPO’s decoupled approach reduces computational costs and improves training stability. It offers a practical alternative to existing RLHF methods and opens new avenues for exploring decoupled optimization techniques in LLMs.

Visual Insights

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🔼 This figure compares the architectures of Decoupled Value Policy Optimization (DVPO) and Proximal Policy Optimization (PPO) in the context of Reinforcement Learning from Human Feedback (RLHF). It highlights that DVPO replaces the need for a separate reward model with a pre-trained global value model, decoupling the policy and value learning phases. In contrast, PPO requires joint training of both a policy and a value function, guided by a separately trained reward model. Both methods utilize offline data, but DVPO doesn’t require any additional ground truth reward signals during policy optimization.

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Figure 1: Overview of Decoupled Value Policy Optimization (DVPO) and PPO in RLHF. DVPO eliminates the need for a reward model and decouples policy and value learning during policy optimization. In contrast, PPO requires training a reward model before policy optimization. DVPO instead trains a global value model using the same offline data as the reward model. During policy training, no additional ground-truth rewards are obtained.

🔼 This table presents a comparison of the performance of different models on three benchmark tasks (MT-Bench, Arena-Hard, and AlpacaEval) under the Base setting. The Base setting involves using the Ultrafeedback dataset and starting from an SFT (Supervised Fine-Tuning) model. Two different base models were used: llama3.2-3B-Base (indicated by ‘3B’) and llama3-8B-Base (indicated by ‘8B’). For each base model, three models are evaluated: the original SFT model, a model optimized with PPO (Proximal Policy Optimization), and a model optimized with DVPO (Decoupled Value Policy Optimization). The table shows that DVPO consistently outperforms both SFT and PPO across all benchmarks and model sizes, demonstrating its effectiveness in improving model performance.

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Table 1: Performance under the Base setting. The suffix “3B” indicates that llama3.2-3B-Base was used as the base model, while “8B” indicates that llama3-8B-Base was used as the base model. Both PPO and DVPO were initialized from SFT and optimized. Compared to PPO, DVPO achieved the best benchmark scores across models with different parameter sizes.

In-depth insights

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Lean Value RL

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Lean Value Reinforcement Learning (LVRL) likely refers to a paradigm shift in RL, prioritizing computational efficiency and stability without sacrificing performance. This likely involves reducing model complexity, perhaps by decoupling actor-critic architectures or simplifying reward structures. LVRL could focus on pre-training value functions to minimize online learning, enabling faster convergence. The core idea of LVRL is to achieve state-of-the-art performance using significantly fewer resources, this approach is critical for real-world applications where computational constraints are prevalent. This approach may use knowledge distillation or model compression techniques to create smaller value functions that can be easily deployed. Emphasis on interpretability is another key aspect, making the model transparent and debuggable.

Decoupled DVPO

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Decoupled DVPO proposes an innovative approach to reinforcement learning from human feedback (RLHF), addressing key limitations of traditional methods. By pre-training a global value model (GVM) separate from the policy optimization, DVPO breaks the tight loop of actor-critic training, leading to increased stability and reduced computational cost. The GVM, conditioned on policy trajectories, provides token-level return-to-go estimates, offering fine-grained guidance during policy updates. Unlike methods requiring joint training, DVPO fixes the GVM, promoting stability and enabling efficient offline use of data. This design choice mitigates the “moving target” problem in actor-critic methods, leading to more predictable policy improvements. DVPO is especially advantageous in offline RLHF scenarios, where additional environmental rewards cannot be collected. By decoupling, DVPO maintains fine-grained supervision while substantially lowering training complexity and resource consumption, offering a promising avenue for scalable and stable RLHF.

Fixed GVM Guide

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Fixed GVM guide maintains stability in policy updates by freezing the Global Value Model (GVM), eliminating the ‘moving target’ problem of actor-critic methods. This leads to more stable and predictable policy updates. It also enables efficient offline use, as the static GVM provides all necessary supervisory information, facilitating the reuse of offline datasets for policy optimization. Freezing the GVM after pre-training ensures that the policy optimization process relies on a stable and consistent value function, thereby reducing instability and improving convergence.

No New Rewards

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The absence of fresh environment rewards during policy training is a crucial constraint in offline RLHF. Because neither the reward model nor the global value model (GVM) can be updated with new data reflecting policy changes, they essentially act as static supervisors. This limitation implies that any improvements achieved stem solely from effectively utilizing the existing offline dataset. The proof emphasizes that, under these conditions, the choice between using a reward model or a GVM for guidance becomes a matter of approximation accuracy within the offline data distribution. As approximation errors diminish, the policy gradients derived from either model converge, leading to equivalent optimization outcomes. This equivalence highlights the importance of thorough pre-training of either model to capture the nuances of the desired behavior from the fixed dataset, as no further refinement based on interaction is possible.

Better Feedback

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While not explicitly present, the concept of ‘better feedback’ is intrinsically linked to the research. The transition from traditional RLHF, reliant on a fixed reward model, to DVPO, which employs a pretrained global value model (GVM), embodies the pursuit of enhanced feedback mechanisms. The GVM, conditioned on policy trajectories, generates token-level return-to-go estimates, offering a more granular and nuanced assessment compared to the coarse, sentence-level rewards often used. This finer-grained feedback empowers the policy to learn more effectively, enabling the model to discern subtle preferences and adapt its behavior accordingly. The paper’s focus on improving training efficiency, stability, and convergence speed indirectly aims at better feedback loops. A more stable and efficient training process allows for more rapid iteration and refinement, leading to improved alignment with human preferences. Furthermore, the equivalence between pretraining a reward model and a GVM suggests that the key to better feedback lies in capturing comprehensive information from offline data. The GVM approach represents a shift towards leveraging richer, more informative signals to guide policy optimization, ultimately resulting in models that are better aligned with human values and preferences.

More visual insights

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Full paper

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