ashishk1331/ccd-repro-code

Reproduction: "Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models"

Independent reproduction of ICML 2026 paper #6883 (OpenReview b0O96emqNj, arXiv 2512.02044). No official code was released, so everything here is written from the equations in Section 3.

Layout

path	what
scripts/ccd_decode.py	CCD + CCD-DS decoder for Dream (Eq. 1, 6, 16, 17, 18, 20)
scripts/run_eval.py	Trip Plan / HumanEval driver, Dream's official hyperparameters
scripts/check_propositions.py	Claim 2: exact numerical audit of Prop. 1 and Prop. 2
scripts/check_budget_bound.py	the k <= V/(d+1) speedup ceiling + simulation
scripts/smoke_test.py	decoder mechanics on a tiny random Dream (CPU)
scripts/test_scorers.py	metric validation against gold answers (CPU)
scripts/make_figures.py	logbook figures + raw CSVs
scripts/job_*.sh	the exact HF Jobs run scripts
data/trip_planning.json	Trip Plan benchmark (from DreamLM/Dream eval/data/)
outputs/	all results, per-example scores/steps/responses, figures

Reproducing

pip install torch "transformers==4.46.2" "huggingface_hub<1.0" "datasets<4" accelerate
# free, no GPU: mechanism + metric gates + both analytical claims
python scripts/smoke_test.py
python scripts/test_scorers.py
python scripts/check_propositions.py
python scripts/check_budget_bound.py
# GPU (~24GB): one benchmark config
python scripts/run_eval.py --task trip --method ccd_ds --limit 64 --out outputs/x.json

Determinism / seeds

component	status
check_propositions.py, check_budget_bound.py	Seeded (np.random.default_rng(0)). Bit-identical across runs — verified 3x.
Example selection (load_trip)	Seeded, fixed at seed=0, independent of --seed, so every arm scores the same stratified sample.
Decoding at T=0 (Trip Plan, HumanEval, all ablations)	Deterministic: _pick_token takes the argmax, no RNG involved.
Decoding at T>0 (the Claim 6 sweep, T=0.1/0.4/0.7/1.0)	Samples via Categorical.sample(). run_eval.py --seed N (default 0) now seeds random/numpy/torch, and the seed is recorded in each result JSON.

Honest caveat: --seed was added after the runs in outputs/ were produced. The T=0 results (the large majority, including every number on the poster's ceiling, Trip Plan, HumanEval and ablation cards) are argmax and reproduce exactly regardless. The eight temperature-sweep runs at T>0 (c6_*_t0.1/0.4/0.7/1.0) predate the seeding and therefore will not reproduce bit-exactly; re-running them under --seed 0 is the first thing to do with a fresh GPU budget. At n=16 those points are our weakest evidence anyway, and the poster says so.

Hyperparameters

Taken from the Dream authors' own eval scripts, which is what the paper says it does ("we follow the base models' default settings without tuning"):

• Trip Plan: steps=256, max_new_tokens=256, temperature=0, top_p=1, alg=entropy, 2-shot (DreamLM/Dream eval/eval_dream_gen_planning.sh)
• HumanEval: steps=768, max_new_tokens=768, temperature=0.1, top_p=0.9, alg=entropy, 0-shot chat template (DreamLM/Dream eval_instruct/eval.sh)

CCD: V=4, d=3 for Dream (paper Sec. 4.2).

Gotchas found

• The Trip Plan file is ordered by difficulty (first 200 examples are all num_cities=3). Prefix sampling inflates the score ~55% vs ~15%. Use the stratified sampler in load_trip.
• HumanEval responses are the function body only (the prompt's gen_prefix already contains the signature), so the \ndef stop sequence must be applied to the continuation, not to prompt+continuation.