QLoRA adapter trained with regularized SPARK-style co-evolution on the full 311-problem MBPP pool over 6 iterations. A cautionary result: the regularized recipe delays but does not prevent drift over a longer schedule — HumanEval pass@1 regresses −2.2 pp and KL climbs to ~0.096 by iteration 6.

TL;DR

spark-code-C-reg2-3b extends the regularized co-evolve recipe (spark-code-C-reg-3b) to the full 311-problem MBPP pool and a 6-iteration schedule, with slightly stronger KL regularization (kl_coeff=0.03) and a smaller auxiliary loss (aux_loss_scale=0.02, pairwise weight dropped to 0.05). Unlike the shorter 3-iteration C-reg run — which matched the exec-only baseline within noise — this longer run drifts: HumanEval pass@1 falls from 0.796 to 0.774 (−2.2 pp), GRPO KL climbs steadily to ~0.096, and mean completion length contracts ~54%. The held-out MBPP pass@5 peaks early (0.71 at iter 3) and decays back to baseline by iter 6. The published weights are the completed iteration-6 state. This card documents a negative/cautionary result: more iterations are not better for the co-evolve recipe; the sweet spot here was ~iteration 3.

Training Setup

Base model: Qwen/Qwen2.5-Coder-3B-Instruct
Method: GRPO (exec-only reward, partial per-test scoring, frozen-reference KL) + auxiliary SFT phase per iteration. Auxiliary examples mined from each iteration's rollouts:
- Pointwise — binary "Correct/Incorrect" judgments over a single candidate
- Pairwise — randomized A/B preference between a passing and a failing rollout
- Reflection — execution-grounded repair, target = a sibling correct rollout or the MBPP canonical solution (reflection_target_mode=correct_or_canonical)
Training data: MBPP-sanitized, 311 problems (full pool), 6 iterations (completed), K=4 adaptive rollouts (up to 8), partial per-test rewards with syntax_penalty=-0.2, runtime_penalty=-0.1, timeout_penalty=-0.3.
LoRA: r=16, alpha=32, dropout=0.05, targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj.
Quantization: 4-bit NF4 + double quant, bf16 compute.
Optimizer: AdamW, lr=5e-6, grad_accum=4, clip_ratio=0.2, max_grad_norm=1.0.
GRPO KL: kl_coeff=0.03 against the frozen reference policy.
Aux hyperparameters (this run): aux_loss_scale=0.02, aux_weight_pointwise=0.0, aux_weight_pairwise=0.05, aux_weight_reflection=1.0, aux_epochs=1, aux_max_len=1024.
Aux pool sizes (after caps): iter 1 → 769 (pointwise 200 / pairwise 200 / reflection 369), iter 2 → 784 (200/200/384), iter 3 → 754 (200/200/354), iter 4 → 738 (200/198/340), iter 5 → 740 (200/200/340), iter 6 → 682 (200/178/304).
Seed: 42.

Training script: run_experiment_with_mbpp_heldout.py in the GitHub repo.

Evaluation Results

HumanEval is evaluated with 5 samples per problem at temperature=0.2, top_p=0.95. Held-out MBPP uses 100 problems disjoint from the training pool. "Reflection fix rate" is measured on the HumanEval held-out problems: for each failed first-pass generation the model is asked to repair its own code, and the fix is re-executed.

Iter	HumanEval pass@1	HumanEval pass@5	MBPP-held pass@1	MBPP-held pass@5	Train pass rate	GRPO KL	Refl. fix rate
0	0.796	0.854	0.634	0.680	—	—	0.118
1	0.799	0.848	0.638	0.700	0.593	0.0003	0.061
2	0.788	0.829	0.634	0.690	0.603	0.0157	0.057
3	0.796	0.829	0.644	0.710	0.628	0.0485	0.061
4	0.788	0.817	0.626	0.660	0.646	0.0592	0.059
5	0.771	0.823	0.628	0.670	0.657	0.0822	0.081
6	0.774	0.823	0.632	0.680	0.696	0.0957	0.083

Trajectory. HumanEval pass@1 holds near baseline through iter 3 (0.799 → 0.788 → 0.796), then declines through iters 4–6 to 0.774 (−2.2 pp). Held-out MBPP pass@5 peaks at iter 3 (0.71) and decays back to the baseline 0.68 by iter 6. Two drift signals grow monotonically and explain the regression:

GRPO KL climbs steadily from 3e-4 (iter 1) to 0.096 (iter 6) — roughly a 40× the matched exec-only run (A-v2, KL ~0.0024 at iter 5). The auxiliary objective accumulates off-distribution pressure that the kl_coeff=0.03 term only partly contains.
Completion length contracts ~54% — mean tokens per GRPO sequence fall from 185 (iter 1) to 86 (iter 6), the same shortening signature seen in the naive co-evolve run, here arriving more slowly.

The reflection fix rate stays low throughout (0.06–0.08), below the untrained baseline (0.118). Train pass rate keeps rising (0.593 → 0.696) even as held-out HumanEval falls — i.e. the model is fitting the training pool while losing cross-benchmark generalization.

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-3B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, "amarsaikhan/spark-code-C-reg2-3b")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")

prompt = tok.apply_chat_template(
    [{"role": "system", "content": "You are an expert Python programmer. Return only correct Python code."},
     {"role": "user", "content": "Write a Python function is_palindrome(s) that returns True if s reads the same forwards and backwards."}],
    tokenize=False, add_generation_prompt=True,
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.2, do_sample=True, top_p=0.95)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Comparison to Other Conditions

All five adapters share the same base model and seed. The original three used a 200-problem pool over 3 iterations; the two -v2/2 adapters use the full 311-problem pool over 6 iterations. Each row reports that adapter's published checkpoint.

Condition	Pool / iters	aux_loss_scale	kl_coeff	HumanEval pass@1	MBPP-held pass@5
C-reg2 (regularized, full) — this card	311 / it 6	0.02	0.03	0.774	0.680
A-v2 (exec-only, full)	311 / it 4	0.00	0.02	0.816	0.710
A (exec-only)	200 / it 3	0.00	0.01	0.805	0.690
C-reg (regularized)	200 / it 3	0.03	0.02	0.800	0.710
C-light (naive)	200 / it 3	0.10	0.01	0.773	0.680

At its published checkpoint C-reg2 sits at the bottom on HumanEval pass@1, tied with the naive co-evolve run — the longer schedule erased the stability advantage that the shorter C-reg run had.

Findings Summary

Regularization delays drift; it does not prevent it. Over 6 iterations the regularized recipe still drifts (KL → 0.096, completion length −54%) and regresses on HumanEval (−2.2 pp). The shorter 3-iteration C-reg matched the baseline precisely because it stopped before the drift compounded.
The auxiliary objective is the destabilizer, not the schedule. The matched exec-only run on the same 311-pool / 6-iteration budget (A-v2) stayed at KL ~0.0024 and reached the study's best HumanEval (0.816). Same data, same length, no aux → no drift.
The operating sweet spot was ~iteration 3. Held-out MBPP pass@5 peaked at 0.71 (iter 3) and HumanEval was still at baseline there. An early-stopping or checkpoint-at-iter-3 policy would have captured the recipe's benefit without the later regression. The published iter-6 weights are the completed run, not the best checkpoint.

Related Artifacts

Sibling adapters: spark-code-A-3b-v2 · spark-code-A-3b · spark-code-C-light-3b · spark-code-C-reg-3b
GitHub repository: https://github.com/amarsaikhanb/spark-code
Full per-problem eval data (HumanEval, held-out MBPP, and reflection JSONs, iters 0–6) lives under condition_C/eval/ in the repository
Interactive demo Space: [SPACES_URL]

Citation

bibtex

@misc{batjargal2026sparkcode,
  title  = {SPARK-Code: Co-Evolving Policy and Reward for Code Generation},
  author = {Amarsaikhan Batjargal},
  year   = {2026},
}

License

The LoRA adapter weights in this repository are released under the Apache 2.0 license. The base model, Qwen/Qwen2.5-Coder-3B-Instruct, is distributed under the Tongyi Qianwen LICENSE; any downstream use must comply with its terms.