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AutogradC

Fresh

Reimplement HIPS/autograd's automatic differentiation from scratch in C: a tensor expression-graph engine computing value/grad/Jacobian/JVP/Hessian-vector-product, graded server-side against an autograd-generated corpus.

Type
RL Env
Publisher
General Reasoning
Runtime
ORS
License
unknown
Size
1 tasks
Published
Jun 2026
Updated
Nov 2026
Canonical
openreward.ai/GeneralReasoning/AutogradC

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AutogradC

A "harness" OpenReward environment: the agent re-implements the core of HIPS/autograd (automatic differentiation) from scratch in C. The artifact autogradc reads one JSON case (a tensor expression graph + inputs + a derivative task) on stdin and prints the value / gradient / Jacobian / JVP / Hessian-vector product on stdout. It is graded against a corpus whose answers were computed offline by the real Python autograd.

  • Spec / IO contract: build/PROTOCOL.md
  • Design + reward + anti-cheat: DESIGN.md
  • Env class (server-side grading): autogradc.py
  • Producer (sandbox, bwrap): harness/ (Rust ag-runner)
  • Corpus oracle (offline, author-run): build/generate_corpus.py → commits build/corpus/; build/filter_corpus.py splits it into both images.
  • Reference solution (proves the reward): reference_solution/

Layout

autogradc.py            env class: tools (run_tests/progress), server-side grade_case
server.py               OpenReward server entrypoint
Dockerfile              env-server image (holds the expected answers)
sandbox.Dockerfile      sandbox image (C toolchain + corpus inputs + ag-runner)
harness/                Rust producer: runs the artifact per case under bubblewrap
build/PROTOCOL.md       the IO contract (single source of truth)
build/generate_corpus.py  OFFLINE oracle (needs autograd); writes build/corpus/
build/filter_corpus.py  splits committed build/corpus into the image layout
build/corpus/           COMMITTED generated corpus (inputs + expected.jsonl)
reference_solution/     from-scratch C reference (Phase-4 validation)
golden_tests.py         no-Docker invariant tests (telescoping reward, anti-cheat)

Workflow

  1. Generate the committed corpus once (author-time): uv run --with autograd --with numpy python build/generate_corpus.py
  2. uv run python -m pytest golden_tests.py -v (no Docker)
  3. docker build -f sandbox.Dockerfile -t autogradc:dev . (needs build/corpus committed)
  4. Reference-solution check: build reference_solution/ into a workspace, grade against the corpus → ~full; an empty stub → ~0.
  5. Publish: connect the repo to OpenReward (server image) + push to main (the workflow builds + pushes the sandbox image to GHCR); pin SANDBOX_IMAGE to the printed :sha-<gitsha>.