FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

2024-11-20 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

FBOS-RL, a Feedback-Driven Bi-Objective Synergistic Reinforcement Learning framework, addresses the inefficiency of mainstream RL algorithms like GRPO in large language model (LLM) training. It introduces Feedback-Guided Exploration Enhancement, where a rule-based verifier provides natural-language feedback on initial rollouts, creating Feedback-Augmented Prompts (FAPs) for a second sampling round. This framework then optimizes two mutually reinforcing objectives: Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC). Experiments on TravelPlanner and MiniF2F-Lean4 datasets, using models like Llama-3.1-8B-Instruct, Qwen3-14B, and Qwen3.5-27B, show FBOS-RL significantly improves training efficiency and performance ceiling, maintains higher policy entropy, and exhibits lower gradient norms compared to GRPO and controlled baselines, also demonstrating OOD generalization to GPQA-Diamond.

Key takeaway

For Machine Learning Engineers optimizing large language models with reinforcement learning, FBOS-RL offers a robust solution to overcome training stagnation and improve performance. By integrating feedback-guided exploration and dual-objective optimization, you can achieve faster learning, higher performance ceilings, and better training stability. Consider implementing Feedback-Augmented Prompts and the EPA/ECC objectives to enhance your LLM's reasoning capabilities, especially for complex tasks where traditional GRPO struggles.

Key insights

Feedback-driven bi-objective reinforcement learning significantly boosts LLM training efficiency and performance by synergizing exploitation and exploration.

Principles

• Feedback-guided exploration enhances rollout quality for better gradient direction.
• Mutually reinforcing objectives create a positive bootstrapping effect.
• Higher policy entropy correlates with stronger exploration capability.

Method

FBOS-RL involves initial exploration, feedback-guided exploration enhancement using Feedback-Augmented Prompts (FAPs), and bi-objective synergistic training with Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC).

In practice

• Use rule-based verifiers to generate natural language feedback.
• Implement FAPs for targeted second-round sampling.
• Co-optimize policy alignment and capability cultivation objectives.

Topics

• Reinforcement Learning
• Large Language Models
• Policy Optimization
• Feedback Mechanisms
• Exploration-Exploitation
• GRPO

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.