Authors: Swarnadeep Saha, Archiki Prasad, Justin Chih-Yao Chen, Peter Hase, Elias Stengel-Eskin, Mohit Bansal
Abstract: Language models can be used to solve long-horizon planning problems in two
distinct modes: a fast ‘System-1’ mode, directly generating plans without any
explicit search or backtracking, and a slow ‘System-2’ mode, planning
step-by-step by explicitly searching over possible actions. While System-2 is
typically more effective, it is also more computationally expensive, making it
infeasible for long plans or large action spaces. Moreover, isolated System-1
or 2 ignores the user’s end goals, failing to provide ways to control the
model’s behavior. To this end, we propose the System-1.x Planner, a
controllable planning framework with LLMs that is capable of generating hybrid
plans and balancing between the two planning modes based on the difficulty of
the problem at hand. System-1.x consists of (i) a controller, (ii) a System-1
Planner, and (iii) a System-2 Planner. Based on a user-specified hybridization
factor (x) governing the mixture between System-1 and 2, the controller
decomposes a problem into sub-goals, and classifies them as easy or hard to be
solved by either System-1 or 2, respectively. We fine-tune all three components
on top of a single base LLM, requiring only search traces as supervision.
Experiments with two diverse planning tasks — Maze Navigation and Blocksworld
— show that our System-1.x Planner outperforms a System-1 Planner, a System-2
Planner trained to approximate A* search, and also a symbolic planner (A*). We
demonstrate the following key properties of our planner: (1) controllability:
increasing the hybridization factor (e.g., System-1.75 vs 1.5) performs more
search, improving performance, (2) flexibility: by building a neuro-symbolic
variant with a neural System-1 and a symbolic System-2, we can use existing
symbolic methods, and (3) generalizability: by being able to learn from
different search algorithms, our method is robust to the choice of search
algorithm.
Source: http://arxiv.org/abs/2407.14414v1
