user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Code Synthesis with Priority Queue Training

Paper content:
We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards.
We introduce a novel iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far.
Then, we synthesize new programs and add them to the priority queue by sampling from the RNN.
We benchmark our algorithm called priority queue training (PQT) against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF.
Our experimental results show that our deceptively simple PQT algorithm significantly outperforms the baselines.
By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.
Automatic program synthesis is an important task with many potential applications.
Traditional approaches (e.g., BID29 ; BID1 ) typically do not make use of machine learning and therefore require domain specific knowledge about the programming languages and hand-crafted heuristics to speed up the underlying combinatorial search.
To create more generic programming tools without much domain specific knowledge, there has been a surge of recent interest in developing neural models that facilitate some form of memory access and symbolic reasoning (e.g., BID37 ; BID33 ; BID21 ; BID49 ; ).
Despite several appealing contributions, none of these approaches is able to synthesize source code in an expressive programming language.More recently, there have been several successful attempts at using neural networks to explicitly induce programs from input-output examples BID8 BID2 BID35 and even from unstructured text BID35 , but often using restrictive programming syntax and requiring supervisory signal in the form of ground-truth programs or correct outputs.
By contrast, we advocate the use of an expressive programming language called BF 1 , which has a simple syntax, but is Turing complete.
Moreover, we aim to synthesize programs under the reinforcement learning (RL) paradigm, where only a solution checker is required to compute a reward signal.
Furthermore, one can include a notion of code length penalty or execution speed into the reward signal to search for short and efficient programs.
Hence, the problem of program synthesis based on reward is more flexible than other formulations in which the desired programs or correct outputs are required during training.To address program synthesis based on a reward signal, we study two different approaches.
The first approach is a policy gradient (PG) algorithm BID44 , where we train a recurrent neural network (RNN) to generate programs one token at a time.
Then, the program is executed and scored, and a reward feedback is sent back to the RNN to update its parameters such that over time better programs are produced.
The second approach is a deceptively simple optimization algorithm called priority queue training (PQT).
We keep a priority queue of K best programs seen during training and train an RNN with a log-likelihood objective on the top K programs in the queue.
We then sample new programs from the RNN, update the queue, and iterate.
We also compare against a genetic algorithm (GA) baseline which has been shown to generate BF programs BID3 .
Surprisingly, we find that the PQT approach significantly outperforms the GA and PG methods.We assess the effectiveness of our method on the BF programming language.
The BF language is Turing complete, while comprising only 8 operations.
The minimalist syntax of the BF language makes it easier to generate a syntactically correct program, as opposed to more higher level languages.
We consider various string manipulation, numerical, and algorithmic tasks.
Our results demonstrate that all of the search algorithms we consider are capable of finding correct programs for most of the tasks, and that our method is the most reliable in that it finds solutions on most random seeds and most tasks.The key contributions of the paper include,• We propose a learning framework for program synthesis where only a reward function is required during training (the ground-truth programs or correct outputs are not needed).Further
, we advocate to use a simple and expressive programming language, BF, as a benchmark environment for program synthesis (see also BID3 ).• We propose
an effective search algorithm using a priority queue and an RNN.• We propose
an experimental methodology to compare program synthesis methods including genetic algorithm and policy gradient. Our methodology
measures the success rates of each synthesis method on average and provides a standard way to tune the hyper-parameters.With this methodology, we find that a recurrent network trained with priority queue training outperforms the baselines.
In this paper, we considered the task of learning to synthesize programs for problems where a reward function is defined.
We use an RNN trained with our priority queue training method.
We experimented with BF, a simple Turing-complete programming language, and compared our method against a genetic algorithm baseline.
Our experimental results showed that our method is more stable than vanilla policy gradient or a genetic algorithm.That PQT works as a standalone search algorithm is surprising, and future work is needed in order to better explain it.
We can speculate that it is implementing a simple hill climbing algorithm where the buffer stores the best known samples, thereby saving progress, while the RNN functions as an exploration mechanism.
Even more surprising is that this algorithm is able to bootstrap itself to a solution starting from an empty buffer and a randomly initialized RNN.
We believe that our coding environment complements the PQT algorithm, since finding code with non-zero reward through purely random search is feasible.

assistant: text: We use a simple search algorithm involving an RNN and priority queue to find solutions to coding tasks.