Replaying Failures for Efficient Instruction Following in RL
Analysis
This paper addresses the sample inefficiency problem in Reinforcement Learning (RL) for instruction following with Large Language Models (LLMs). The core idea, Hindsight instruction Replay (HiR), is innovative in its approach to leverage failed attempts by reinterpreting them as successes based on satisfied constraints. This is particularly relevant because initial LLM models often struggle, leading to sparse rewards. The proposed method's dual-preference learning framework and binary reward signal are also noteworthy for their efficiency. The paper's contribution lies in improving sample efficiency and reducing computational costs in RL for instruction following, which is a crucial area for aligning LLMs.
Key Takeaways
- •Proposes Hindsight instruction Replay (HiR) to improve sample efficiency in RL for instruction following.
- •Reinterprets failed attempts as successes based on satisfied constraints.
- •Employs a dual-preference learning framework with a binary reward signal for efficient optimization.
- •Demonstrates promising results across various instruction following tasks with reduced computational budget.
“The HiR framework employs a select-then-rewrite strategy to replay failed attempts as successes based on the constraints that have been satisfied in hindsight.”