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README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-classification
+  - text-generation
+language:
+  - en
+tags:
+  - workflow-planning
+  - slm
+  - lora
+  - instruction-tuning
+  - policy-learning
+  - fork-join
+  - multi-agent
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.jsonl
+      - split: validation
+        path: valid.jsonl
+      - split: test
+        path: test.jsonl
+---
+
+# SLM Workflow Planner — Policy-Corrected Instruction Tuning Dataset (v2)
+
+## Overview
+
+High-quality instruction-tuning dataset for training a Small Language Model (SLM) 
+to serve as a **workflow execution planner**. The model learns to make policy-aware 
+decisions about workflow transitions: when to proceed (NEXT), retry (RETRY), 
+parallelize (FORK), synchronize (JOIN), or escalate (META).
+
+## Key Features
+
+- **648K instruction pairs** across 2 stages (decision type + node selection)
+- **Policy-corrected labels**: FORK/JOIN/META decisions are conditioned on state 
+  signals (resource pressure, parallel activity, goal progress, uncertainty), 
+  not just topology flags
+- **Counterfactual negatives**: Scenarios where topology suggests FORK/JOIN but 
+  state signals dictate NEXT — teaching the model true policy boundaries
+- **89 diverse workflow graphs** across 8 structural families + 2 semantic workflows
+- **Balanced decision distribution**: NEXT ~36%, JOIN ~27%, META ~13%, FORK ~12%, RETRY ~12%
+
+## Dataset Structure
+
+Each sample is a chat-completion format message with 3 roles:
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": "You are a workflow planner..."},
+    {"role": "user", "content": "Current node: ... State: ... Eligible: ..."},
+    {"role": "assistant", "content": "FORK"}
+  ]
+}
+```
+
+### Stage 1: Decision Type Classification
+Given current node, outcome, state signals, eligible nodes, forkable sets, and 
+join-ready nodes → predict one of: `NEXT`, `RETRY`, `FORK`, `JOIN`, `META`
+
+### Stage 2: Node Selection
+Given the decision type and candidates → select which node(s) to execute
+
+## State Signals
+
+Each sample includes realistic state signals:
+- `goal_progress` (0→1): How close to workflow completion
+- `retry_count` / `total_retries`: Retry budget tracking
+- `parallel_active` / `parallel_depth`: Current parallelism state
+- `resource_pressure` (0→1): System load indicator
+- `sla_pressure` (0→1): Deadline urgency
+- `uncertainty_level` (0→1): Confidence in current path
+- `cost_accrued`: Cumulative cost so far
+- `escalation_count`: Number of escalations triggered
+
+## Policy Boundaries (What Makes This Dataset Special)
+
+Unlike naive topology-based labeling, this dataset includes:
+
+| Scenario | Topology says | State says | Label |
+|----------|--------------|------------|-------|
+| Forkable set present + high resource pressure | FORK | Don't parallelize | **NEXT** |
+| Forkable set present + already parallel | FORK | Already forked | **NEXT** |
+| Join-ready + low goal progress | JOIN | Too early | **NEXT** |
+| Anomaly outcome + high uncertainty | NEXT | Escalate | **META** |
+
+These counterfactual samples are critical for learning **policy** vs **topology**.
+
+## Workflow Families
+
+Generated from 89 workflows spanning:
+1. Linear with optional exits
+2. Retry loops
+3. Fork-Join patterns
+4. OR-alternative paths
+5. Escalation ladders
+6. Partial (k-of-n) joins
+7. Conditional irreversible branches
+8. Long horizon workflows
+9. Semantic parallel (Insurance Claim)
+10. Semantic join patterns
+
+## Intended Use
+
+Fine-tuning SLMs (3B–7B) with LoRA for workflow planning tasks.
+Tested with: `Qwen/Qwen2.5-7B-Instruct`
+
+## Splits
+
+| Split | Samples |
+|-------|---------|
+| Train | 583,504 |
+| Valid | 32,417 |
+| Test | 32,417 |
+| **Total** | **648,338** |
+
+## Citation
+
+Part of the Agentic Factory project — building autonomous workflow orchestration 
+with SLM-powered planning.