CogEvol is a purpose-trained model family that generates classroom slides and self-contained interactive HTML pages in one shot, using an Reinforcement Learning reward whose interactivity term is measured by executing the page, not judged from screenshots. That measurement caught a reward-hacking regression that made games score high while being unplayable, and fixing it restored the game score from 18.8 to 57.6.
You’ve shipped an LLM feature that emits a self-contained artifact, say an interactive React demo or a chart-heavy report, and users edit it iteratively. Today you either paste the whole file back through a coding agent and wait several minutes per turn, or you run a Claude Code-style scaffold that iterates through many tool calls. Both are slow, and both judge success by whether the output looks right rather than whether its buttons actually work. CogEvol argues that for education-style interactive artifacts, neither the general coding agent nor a screenshot-graded model is enough: you need a small purpose-trained model, a strict output contract the model has internalized, and a reward that literally clicks the buttons before scoring the page. The dominant baseline the paper positions against is the coding-agent recipe (a strong general LLM plus a Claude-Code-style scaffold), which the paper measures at roughly 150 seconds per edit on the same backbone versus 6.3 seconds through their harness.
The recipe is post-training only, on top of public Qwen3 base models (a 4B dense and a 27B hybrid attention model). Three stages, in order:
1.
Mixed SFT on 53,687 conversations distilled from real production traffic. Slide targets come from teacher models whose outputs are accepted only after being rendered and judged; interactive-HTML targets come from mining production failures, regenerating them, and re-executing each candidate in a headless Chromium instance before admitting it to the dataset.
2.
Slide RL using Group Relative Policy Optimization (GRPO) with a hybrid reward: 60% from a vision-language judge scoring the rendered slide image, 40% from a hand-written rule engine encoding geometric truths (canvas use, collisions, chart geometry).
3.
Interactive-HTML RL, again with GRPO, but the reward now includes an interactivity probe: a Playwright-driven Chromium instrumenting the page’s Canvas draw calls, holding the page quiescent to establish a baseline of self-running animation, then driving each control and recording which draw signatures are new after the input. A Hard-fail gate zeroes out any page that renders but produces no state change on any operable control.
The pseudo-code for scoring one candidate page during RL:
page = chromium.load(html)
if not page.rendered_ok(): return 0
baseline_sigs = record_canvas_draws(page, quiet_ms=500)
for control in page.enumerate_controls():
drive(control); delta_sigs = new_draws() - baseline_sigs
if hard_fail(delta_sigs, page): return 0 # gate
return weighted_mean(vlm_visual, vlm_content,
viewport_tablet, viewport_mobile,
interactivity_from_delta_sigs)
The formula for the slide reward is plainly: 60% VLM score plus 40% rule score, both on 0-5. The interactive reward is one minus a weighted mean of five defect terms.
The prevailing way to score generated interactive artifacts is to render them and let a vision-language judge rate the screenshot. This paper shows the opposite. Interactivity is not a property of a frame; it is a property of a page’s response to input, and any reward that grades only frames will be gamed by policies that produce beautiful static shells with dead controls. The load-bearing evidence is not the headline score but the reward-hacking A/B: same data, same schedule, only the reward changed, and the game sub-score moved from 18.8 to 57.6.
The single most load-bearing finding is the reward-hacking A/B in Section 4.3. Under the old screenshot-only reward, a 27B checkpoint scored highest of any variant on code and learning-page sub-scores but collapsed to 18.8 on games; humans confirmed the games were unplayable. Retraining from the same checkpoint with the executable interactivity probe and hard-fail gate, changing only the reward, lifted games to 57.6 and 3D pages by +15.9 points. That is the mechanism-proving result.
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On the internal HTML-500 benchmark, CogEvol-27B reaches 63.7 overall with zero hard failures on 500 pages. Claude Opus 4.8 leads the external field at 67.2 but fails 19 of 500 pages outright; Qwen3.8-Max scores 35.3 with 204 of 500 dead at the probe.
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On the internal slide benchmark under the strict rendering contract, no external flagship renders a single slide zero-shot; even after schema normalization the best external zero-shot score sits ~16 points below CogEvol’s SFT-only 4B model.
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A One-Big-Round multi-task RL variant trains both modalities jointly in one epoch with roughly zero forgetting versus the serial recipe, though the serial recipe still wins on HTML in absolute terms.
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On the external PresentBench slide benchmark, CogEvol-27B scores 50.48 mean against 51.82-53.54 for Gemini 3.1 Pro, Claude Sonnet 4.6, and GPT-5.6, closing the gap despite being roughly 27x smaller than the largest coding flagships.
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Reward-human Spearman rank correlation on 128 rated pages reaches 0.749 with the full pipeline, up from 0.672 under earlier judge prompts, mainly by moving scores off the ceiling so the judge can actually discriminate.
Reach for this when you’re shipping a product that generates self-contained interactive artifacts, HTML widgets, dashboards, embedded simulations, and you already have a headless-browser verifier in CI. The paper’s transferable move is to promote that verifier from a post-hoc quality check into the RL reward itself: instrument Canvas or DOM changes, hold the page quiescent to subtract self-running animation, drive every control the probe can operate, and add a hard-fail gate that zeroes any page that renders but never mutates state on input. Combined with a small purpose-trained model, this gets you single-pass generation in tens of seconds instead of the minutes a general coding agent spends iterating.
CogEvol-4B is released open-weight under Apache 2.0, including a Q4_K_M GGUF build for on-device use and the MAIC-UI editing harness, at GitHub. CogEvol-27B is API-only. The evaluation suites (slide-std, slide-short, HTML-500) and judge prompts are not released; the authors run external models through their harness on request. So the artifact you can actually use is the small model plus the editing harness plus, most usefully, the recipe for the executable reward.
If your reward can’t press the button, your model will learn to draw a button that doesn’t work.
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The interactivity probe is scripted, not agentic: it explicitly whitelists controls it cannot operate (drag-and-drop, multi-step gestures, typed-format fields) and excludes them from scoring. A general web agent would score more faithfully but costs an order of magnitude more time per page, which the authors say is incompatible with per-step RL reward latency.
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The core evaluation suites and the VLM judge prompts are internal. External comparisons rely on the authors running third-party APIs through their harness, and the HTML judge is from the same model family (Qwen3.8-27B) as one of the compared flagships (Qwen3.8-Max), which the authors disclose but do not fully quantify as a bias.
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Defects the reward doesn’t price, notably language mixing and cross-page theme drift, do not improve across training rounds. This is consistent with the paper’s own thesis: what the reward cannot measure, RL will not fix, and possibly quietly destroy.