Large language models (LLMs) can generate code from examples without being
\nlimited to a DSL, but they lack search, as sampled programs are independent. In
\nthis paper, we use an LLM as a policy that generates lines of code and then join
\nthese lines of code to let the LLM implicitly estimate the value of each of these
\nlines in its next iteration. We further guide the policy and value estimation by
\nexecuting each line and annotating it with its results on the given examples. This
\nallows us to search for programs within a single (expanding) prompt until a sound
\nprogram is found, by letting the policy reason in both the syntactic (code) and
\nsemantic (execution) space. We evaluate within-prompt search on straight-line
\nPython code generation using five benchmarks across different domains (strings,
\nlists, and arbitrary Python programming problems). We show that the model uses
\nthe execution results to guide the search and that within-prompt search performs
\nwell at low token budgets. We also analyze how the model behaves as a policy and
\nvalue, show that it can parallelize the search, and that it can implicitly backtrack
\nover earlier generations<\/p>\n","protected":false},"excerpt":{"rendered":"
Large language models (LLMs) can generate code from examples without being limited to a DSL, but they lack search, as sampled programs are independent. 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