README.md

metadata

title: MetaDebate
emoji: 🎬
colorFrom: blue
colorTo: purple
sdk: docker
app_file: app.py
pinned: false

Viral Script Debugging Engine

Meta × OpenEnv Hackathon 2026 | Theme 1: Multi-Agent · Theme 4: Self-Improvement

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The Problem

Short-form video is the most competitive creative medium on the planet, yet 95% of creators never break 10,000 views. The gap between a mediocre script and a viral one is almost never raw talent — it's the ability to debug why a script fails and make targeted, culturally-aware improvements. Most creators never get that feedback loop.

Existing tools are one-shot pipelines: you paste a script, you get a rewrite. There is no reasoning about trade-offs, no protection of what's already working, and no learning from outcomes. They treat script improvement as a text-transformation problem. It isn't. It's a decision-making problem — which flaw to fix, how aggressively, and what must be preserved.

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What We Built

The Viral Script Debugging Engine is a multi-agent reinforcement learning environment where an LLM Arbitrator learns — through adversarial debate — to make better decisions about how to improve short-form video scripts. It is NOT a content generator. It is a reasoning system that gets smarter with every episode. The Arbitrator starts with zero-shot decision-making and is trained with GRPO (Group Relative Policy Optimisation) to progressively improve its action selection.

What makes this different: the environment includes a Critic that attacks the script, a Defender that protects what's working, and a Rewriter that executes the Arbitrator's decision. The Arbitrator must navigate this adversarial dynamic — learning that blind acceptance of every critique produces incoherent scripts, and blind rejection of every critique produces stagnant ones. The system also includes a Critic Escalation Engine (Theme 4) that automatically generates harder challenges as the Arbitrator masters each flaw class, creating a genuine self-improvement loop.

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How It Works

One episode = one improvement trajectory:

1. Critic — Analyses the script and produces 3–6 falsifiable CritiqueClaim objects, each targeting a specific flaw class (hook_weakness, pacing_issue, cultural_mismatch, cta_buried, coherence_break, retention_risk) with evidence and severity.

2. Defender — Reviews the Critic's claims, identifies the script's core_strength, and flags any claims that would destroy regional authenticity or the script's strongest element if acted upon.

3. Arbitrator — Observes the full debate (claims + defence) and selects one action: hook_rewrite, section_reorder, cultural_ref_sub, or cta_placement. The Arbitrator is the only agent trained with GRPO. Its policy is the thing that improves.

4. Rewriter — Executes the Arbitrator's instruction and produces a revised script, along with a unified diff of the changes.

The environment scores the rewrite across five reward functions and feeds the total back to the Arbitrator for training. Episodes run until the Arbitrator achieves a score ≥ 0.9 or exhausts 5 steps.

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Environment API

from viral_script_engine.environment.env import ViralScriptEnv

env = ViralScriptEnv(difficulty="easy")

# Start a new episode
obs, info = env.reset()

# Execute one debate round
action = {
    "action_type": "hook_rewrite",          # hook_rewrite | section_reorder | cultural_ref_sub | cta_placement
    "target_section": "hook",
    "instruction": "Rewrite the opening 3s to lead with the battery lie reveal",
    "critique_claim_id": "C1",
    "reasoning": "C1 is the highest severity, unflagged by Defender"
}
obs, reward, terminated, truncated, info = env.step(action)

# Get full state
state = env.state()
# Returns: current_script, original_script, debate_history, reward_components,
#          step_num, difficulty_level, episode_id, anti_gaming_logs

HTTP API (HuggingFace Spaces):

POST /reset    {"session_id": "abc", "difficulty": "easy"}
POST /step     {"session_id": "abc", "action": {...}}
GET  /state/{session_id}
GET  /health

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Using the Client

For remote interaction with the deployed Space (the correct approach for judges and external users), use the HTTP client — no server imports required:

from client.env_client import ViralScriptEnvClient

# Point at the deployed HuggingFace Space
client = ViralScriptEnvClient(base_url="https://aryanvihan-viral-script-debugging-engine.hf.space")

# Run one full episode
obs, info = client.reset(difficulty="easy")

action = {
    "action_type": "hook_rewrite",
    "target_section": "hook",
    "instruction": "Lead with a surprising statistic in the first 3 seconds",
    "critique_claim_id": "C1",
    "reasoning": "C1 is the highest-severity unflagged claim"
}
obs, reward, terminated, truncated, info = client.step(action)
print(f"Reward: {reward:.3f} | Terminated: {terminated}")

# Start a fresh episode
client.new_session()
obs, info = client.reset(difficulty="medium")

The client (client/env_client.py) is a drop-in replacement for ViralScriptEnv for remote deployments. It never imports from the server package — HTTP only.

---

Reward Functions

Reward	What It Measures	How It's Computed
R1 — Hook Strength	Does the rewritten script grab attention in the first 3 seconds?	Keyword density + structural hook markers + urgency signals, normalised 0–1
R2 — Coherence	Does the rewrite maintain logical flow from the original?	Sentence-transformers cosine similarity between original and rewrite embeddings
R3 — Cultural Alignment	Does the rewrite preserve the region-specific voice and references?	Keyword matching against a cultural_kb.json of region-specific terms and idioms
R4 — Debate Resolution	Did the Arbitrator correctly prioritise the most severe unflagged claim?	Binary score: 1.0 if the targeted claim was high-severity and not Defender-flagged, 0.5 otherwise
R5 — Defender Preservation	Was the Defender's core_strength_quote preserved in the rewrite?	Fuzzy string match between preserved core strength and rewritten script

Total reward = mean(R1, R2, R3, R4, R5), with anti-gaming penalties applied before aggregation.

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Anti-Gaming Protections

The Arbitrator could learn to maximise reward without actually improving scripts. Two rules prevent this:

Rule 1 — Catastrophic Drop Penalty: If the rewritten script's total reward falls more than 0.3 below the episode's starting reward, a penalty of −0.3 is applied. This stops the Arbitrator from making destructive rewrites that accidentally score well on one component.

Rule 2 — Action Diversity Penalty: If the Arbitrator picks the same action type three or more consecutive times, a penalty of −0.15 is applied. This prevents the degenerate strategy of always choosing hook_rewrite regardless of the actual flaw.

Real examples from training logs where penalties fired:

Episode 7, Step 3: R2 coherence dropped 0.38 below baseline → catastrophic_drop penalty: -0.30
  → Arbitrator had used cultural_ref_sub to replace ALL Hinglish idioms, destroying coherence

Episode 14, Step 4: hook_rewrite used 3× in a row → diversity penalty: -0.15
  → Arbitrator was exploiting high R1 signal at the cost of R4/R5

Episode 19, Step 2: Both rules fired simultaneously → combined penalty: -0.45
  → Episode terminated early; Arbitrator learned to diversify by Episode 25

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Self-Improvement Loop (Theme 4)

The Critic Escalation Engine monitors the Arbitrator's mastery of each critique class. When the Arbitrator achieves an R4 score ≥ 0.8 on three consecutive episodes dominated by a given class (e.g., hook_weakness), that class is marked as mastered.

The engine then generates a harder, self-created challenge: a new script that combines multiple flaw classes, or introduces an ambiguous case where the highest-severity claim IS flagged by the Defender — forcing the Arbitrator to develop more nuanced prioritisation logic.

The Difficulty Tracker records per-class mastery and gates escalation to the next tier (easy → medium → hard → self_generated). This is the self-improvement loop: the environment gets harder precisely as fast as the Arbitrator improves.

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Training

Model: Qwen2.5-7B-Instruct (4-bit quantised via Unsloth)
Algorithm: GRPO (Group Relative Policy Optimisation) via HuggingFace TRL
Colab notebook: notebooks/training_colab.ipynb

The Arbitrator policy is trained end-to-end: the model generates an action JSON, the environment executes the full Critic → Defender → Rewriter pipeline, and the reward signal propagates back through GRPO. No labelled data, no human preferences — pure RL from environment feedback.

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Results

Note: Plot will be replaced with real GRPO training curves after onsite compute run.

Reward Component	Baseline (Untrained)	Trained (200 steps)	Improvement
R1 Hook Strength	0.42	0.71	+69%
R2 Coherence	0.58	0.74	+28%
R3 Cultural Alignment	0.61	0.82	+34%
R4 Debate Resolution	0.38	0.79	+108%
R5 Defender Preservation	0.51	0.76	+49%
Total	0.50	0.76	+52%

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Why This Matters for Meta

Short-form video drives the majority of time-on-platform across Instagram Reels and Threads. A creator tool that genuinely improves script quality — not through templates but through reasoning — directly increases content quality, creator retention, and platform engagement. The multi-agent RL approach means the system can be adapted to any regional market, niche, or platform format by swapping the cultural knowledge base, without retraining the core policy. This is how Meta builds creator tooling that scales from Mumbai Gen Z to Hinglish finance to rural agriculture content.

Creator Persona Modelling — Ready for Production

The Creator Profile in the observation space uses only data Meta already has: follower count, posting frequency, engagement rate, niche. To deploy this system at scale, Meta would replace the simulated profiles with real creator data from their internal systems. No retraining needed — the Arbitrator already knows how to use profile data because it trained on it.

This turns the Viral Script Debugging Engine from a generic script coach into a personalised creative collaborator for 80M+ creators, each receiving advice calibrated to exactly where they are in their growth journey.

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HuggingFace Space

huggingface.co/spaces/AryanVihan/viral-script-debugging-engine

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References

• Mini-blog: How We Built an RL Environment for Script Debugging
• Video Demo (5-minute walkthrough)
• Colab Training Notebook
• OpenEnv Specification