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Parametric learning in your code harness.
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TOML
at letta-code
12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152# Copy this file to config.toml. Generated data and runs are gitignored.
[dataset]# Downloaded from https://www.kaggle.com/datasets/dheerajmpai/projecteulerarchive = "~/Downloads/euler-questions.zip"prepared_dir = "data"
[model]pi_command = "pi"# Examples:# provider = "tinker"# model = "thinkingmachines/Inkling-Small:peft:262144"# provider = "zai"# model = "glm-5.3"provider = "tinker"model = "thinkingmachines/Inkling-Small:peft:262144"thinking = "high"# Set this to a checkpoint alias/hash when a proxy model ID stays constant.revision = ""
[evaluation]languages = ["python", "javascript", "julia"]strategies = ["derive", "bruteforce_then_optimize", "tests_first", "recurrence", "readable"]# Use 2 for a cheap smoke test. A serious gate should use at least 8-16.fanout_per_language = 2max_concurrency = 2timeout_seconds = 600
[sandbox]# Pi's orchestration process remains on the host. Its read/write/edit/bash effects# are workspace-confined and execute through this networkless Docker container.enabled = trueimage = "volcengine/sandbox-fusion@sha256:dd7ff53d16132a8acad6d5da7f15154bb4a331381567a4cb21b3e97ce581f5f9"platform = "linux/amd64"memory = "768m"cpus = "1.0"pids_limit = 64
[curriculum]start_problem = 1state_file = "data/curriculum-state.json"# For fanout=2 this keeps the example runnable. Raise to 6 with fanout=8.required_correct_per_language = 1required_consecutive_rounds = 2regression_window = 3minimum_regression_rate = 0.8require_successful_execution = true# functional_v2 requires a successful write/edit, zero-exit execution of that same# relative file, exact output, a semantically correct response, no escape attempt,# clean completion, and Docker isolation.qualification_mode = "functional_v2"