Thinking Forward and Backward: Effective Backward Planning with Large Language Models
This addresses a specific bottleneck in LLM-based planning for tasks with inherent asymmetries, such as those with bottlenecks near the goal, offering incremental improvements over existing forward-planning methods.
The paper tackled the problem of poor backward planning performance in large language models (LLMs) by proposing a backward planning algorithm that flips the problem to plan forward, which improved planning success rates by 4-24% across three domains.
Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymmetry such that planning backward from the goal is significantly easier -- for example, if there are bottlenecks close to the goal. We take inspiration from this observation and demonstrate that this bias holds for LLM planning as well: planning performance in one direction correlates with the planning complexity of the problem in that direction. However, our experiments also reveal systematic biases which lead to poor planning in the backward direction. With this knowledge, we propose a backward planning algorithm for LLMs that first flips the problem and then plans forward in the flipped problem. This helps avoid the backward bias, generate more diverse candidate plans, and exploit asymmetries between the forward and backward directions in planning problems -- we find that combining planning in both directions with self-verification improves the overall planning success rates by 4-24% in three planning domains.