Supervised Fine-Tuning in Large Language Models: A Novel Approach

Основні поняття

Learning from expert demonstrations can enhance LLM alignment more effectively than preference-based learning.

Анотація

In this article, the author questions the efficacy of preference datasets in aligning Large Language Models (LLMs) and explores the use of expert demonstrations. Various approaches for aligning LLMs using demonstration datasets are introduced, drawing insights from inverse reinforcement learning and imitation learning. The analysis highlights different behaviors of alignment approaches and discusses the pros and cons of supervised fine-tuning. The article delves into Markov Decision Processes, online and offline RL, behavior cloning, imitation learning, and reinforcement learning from human feedback. It also explores different divergence minimization approaches in LLM alignment tasks.

Статистика

P(A ≻ B) = 1/2 + 1/2erf((SA - SB) / sqrt(2(σA^2 + σB^2)))
P(yA ≻ yB|x) = 1/2 + 1/2tanh((rA - rB) / sqrt(2(vA^2 + vB^2)))

Цитати

"We argue that in LLM alignment, learning from demonstration can be more efficient than preference-based learning."
"Aligning LLMs with expert demonstrations can lead to better performance than traditional methods like supervised fine-tuning."

Ключові висновки, отримані з

Supervised Fine-Tuning as Inverse Reinforcement Learning

by Hao Sun о arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12017.pdf

Глибші Запити

How can adversarial imitation learning improve alignment strategies beyond traditional methods

Adversarial imitation learning (AIL) offers a significant improvement in alignment strategies compared to traditional methods by introducing a more robust and flexible approach. One key advantage is the ability of AIL to handle noisy and complex datasets, such as those encountered in Large Language Model (LLM) alignment tasks. Traditional methods like Supervised Fine-Tuning (SFT) often struggle with noisy data, leading to suboptimal performance.
AIL addresses this challenge by incorporating discriminative models that can learn from expert demonstrations without explicitly modeling reward functions. By utilizing adversarial training, AIL can effectively match the state-action occupancy measures or trajectory distributions between expert demonstrations and the current policy. This leads to more stable and reliable learning processes.
Moreover, AIL allows for mode-seeking behaviors through divergences like reverse KL divergence or Jensen-Shannon divergence, providing additional flexibility in optimizing LLM alignment strategies. These mode-seeking behaviors are crucial for open-ended tasks where exploring diverse responses is essential.
Overall, AIL enhances alignment strategies by offering improved handling of noisy data, increased stability during training, and the capability to explore different optimization objectives beyond traditional supervised approaches.

What challenges arise when applying the Bradley-Terry model to RLHF datasets with varying query domains

Applying the Bradley-Terry model to Reinforcement Learning from Human Feedback (RLHF) datasets poses several challenges when dealing with varying query domains:

Domain Heterogeneity: RLHF datasets consist of queries from diverse domains with varying complexities and evaluation criteria. The Bradley-Terry model assumes uniformity in scoring across all responses regardless of domain differences. This lack of domain-specific calibration may lead to inaccuracies in evaluating responses accurately.

Evaluation Variability: In RLHF scenarios, human annotators evaluate responses subjectively based on their understanding and preferences rather than objective criteria like chess moves' outcomes used in Elo ratings. This subjective evaluation introduces variability that may not align well with the assumptions made by the Bradley-Terry model.

Scalability Issues: Unlike traditional Elo ratings applied in games where players are relatively few compared to game instances played over time, RLHF datasets involve numerous query-response pairs evaluated by multiple annotators simultaneously—posing scalability challenges for real-time score adjustments akin to Elo ratings updates after each game.

4 .Offline Learning Limitations: The online nature of updating scores dynamically as new information arrives contrasts with offline learning using preference-based data sets common in RLHF applications employing Bradley-Terry models.

How does the f-divergence framework offer a versatile approach to optimizing LLM alignment through different statistical divergences

The f-divergence framework provides a versatile approach for optimizing LLM alignment through different statistical divergences due...
to its adaptability across various optimization objectives within adversarial imitation learning frameworks:
1 .AIRL - Kullback-Leibler Divergence: By setting f(u) = − log(u), AIRL minimizes KL divergence between state-action occupancy measures ρexp||ρπ.
2 .GAIL - Jensen-Shannon Divergence: GAIL uses f(u) = −(u + 1) log 1+u/2 + u log u function resulting JS divergence between ρexp||ρπ.
3 .FAIRL - Reverse KL Divergence: FAIRL employs f(u)= u log(u), focusing on minimizing reverse KL divergence ρexp||ρπ.
4 .α-IRL - Generalized Divergence: α-IRL introduces a generalized form allowing flexibility based on parameter α values catering towards specific optimization needs.
These diverse choices enable practitioners working on LLM alignments...
to select an appropriate statistical divergence metric aligned with their specific goals while leveraging the robustness offered by the f-divergence framework within adversarial imitation learning paradigms."