AI Evaluation & Planning
Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
Executive Impact
EvalPlanner introduces a novel LLM-as-a-Judge model that learns to plan and reason for evaluation. It generates unconstrained evaluation plans, executes them step-by-step, and arrives at a final judgment. Through a self-training loop with synthetic data, EvalPlanner iteratively optimizes its plans and executions, achieving state-of-the-art performance on benchmarks like RewardBench and PPE, despite using less and synthetically generated training data. This approach highlights the utility of planning and reasoning for building robust LLM-as-a-Judge models.
0%
RewardBench SOTA Accuracy
0%
Performance Improvement
0K
Preference Pairs
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Method Overview
EvalPlanner's approach breaks down evaluation into three components: Evaluation Plan (z), Execution of the Plan (e), and Final Verdict (y). This structured approach allows for more robust and transparent evaluation. The model first generates a detailed evaluation plan specific to the instruction, then executes this plan step-by-step to analyze responses and render a verdict. This disentanglement of planning and execution ensures reasoning follows the defined plan. The system iteratively optimizes both planning and execution through a self-improving loop.
Synthetic Data Generation
Due to the lack of human-annotated CoTs, EvalPlanner relies on synthetic training data. Preference pairs are generated by modifying original instructions ('noisy' instructions) or by sampling multiple responses for math problems where correct solutions are 'chosen' and incorrect ones 'rejected'. Evaluation plans are generated from a seed model conditioned only on the input instruction, ensuring they represent a generic recipe. Plan executions are then generated by prompting the seed model to reason through the plan and responses. This diverse synthetic data is crucial for self-training.
Preference Optimization
EvalPlanner uses a self-training loop with Direct Preference Optimization (DPO). It starts with a seed model, performs supervised fine-tuning (SFT) on a subset of 'chosen' CoTs, and then iterates DPO on preference pairs of CoTs. This teaches the model to contrast between correct and incorrect thoughts, optimizing for both plan generation and execution. The iterative DPO, using fresh subsets of instructions and updated model generations, leads to significant performance improvements over single-iteration training.
93.9%
State-of-the-Art Accuracy on RewardBench
Enterprise Process Flow
User Instruction & Responses
→
Generate Evaluation Plan
→
Execute Plan (Reasoning)
→
Final Verdict (A or B)
| Feature | EvalPlanner | Prior SOTA Models |
|---|---|---|
| Training Data |
|
|
| CoT Generation |
|
|
| Performance (RewardBench) |
|
|
| Generalizability |
|
|
Case Study: Multi-level Constraint Evaluation (FollowBenchEval)
On the challenging FollowBenchEval benchmark, EvalPlanner demonstrated clear superiority, outperforming state-of-the-art models by a significant 13%. This benchmark specifically tests LLM-based judges' ability to plan for and check multi-level, fine-grained constraints (L1-L5). EvalPlanner's learned planning and reasoning capabilities allow it to effectively navigate and verify these complex requirements, showcasing its robust evaluation capabilities beyond subjective preferences.
Advanced ROI Calculator
Estimate the potential cost savings and efficiency gains for your enterprise by implementing intelligent AI evaluation. Adjust the parameters below to see your personalized ROI.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Implementation Timeline
A phased approach to integrate EvalPlanner into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Initial Assessment & Strategy
Understand current evaluation pain points, define target metrics, and strategize EvalPlanner integration tailored to your enterprise's specific LLM-as-a-Judge needs.
Phase 2: Model Training & Customization
Leverage EvalPlanner's self-training capabilities with your existing data (or synthetically generated data) to fine-tune the model for optimal performance in your domain. Customize evaluation plans and execution logic.
Phase 3: Integration & Pilot Deployment
Seamlessly integrate EvalPlanner into your existing MLOps pipeline. Conduct pilot evaluations on a subset of your LLM outputs, gathering initial feedback and refining the system.
Phase 4: Full-Scale Rollout & Continuous Optimization
Deploy EvalPlanner for full-scale LLM evaluation. Implement continuous learning loops to further optimize evaluation plans, reasoning processes, and overall judgment accuracy, ensuring long-term SOTA performance.
Ready to Transform Your AI Evaluation?
Book a personalized session with our AI specialists to explore how EvalPlanner can be tailored to your enterprise’s unique needs and objectives.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat