FRONTIER LABS INITIATIVE

Inference is the
New Training.

Autoregressive parameter scaling is hitting diminishing returns. Acadify's fundamental research unlocks System 2 reasoning by shifting compute from pre-training to test-time search algorithms.

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12x
Inference Scaling

Performance multiplier observed on MATH benchmarks using 10,000-step Monte Carlo Tree Search (MCTS).

50+
Research Clusters

Dedicated engineering teams focusing strictly on logic routing, self-play, and reward models.

Post-Training Paradigms

We are pioneering evaluation frameworks that move beyond rote memorization, forcing models to verify their own logic pathways dynamically.

Process Reward Models

Standard Outcome Reward Models (ORMs) only verify the final answer. We generate high-density PRM datasets that reward the model for every correct step in a deduction chain, preventing correct answers via faulty reasoning.

Step-Level Supervision Logic Verification

Test-Time Compute

Implementing Monte Carlo Tree Search (MCTS) and self-reflection loops during inference. By giving the model computational "time to think," we drastically increase zero-shot performance on complex coding tasks.

System 2 MCTS O1-Style Search

Synthetic Self-Play

Using deterministic verifiers (like Python interpreters or Lean 4 provers) to create an infinite synthetic data loop. Agents generate code, the verifier tests it, and the agent learns directly from the execution trace.

AlphaCode Paradigms Verifiable Truth

Compute
Arbitrage.

The future of AGI is trading massive, expensive pre-training compute for localized inference compute. Giving a smaller model 10 seconds to "think" often outperforms a 10x larger model generating instantly.

  • Overcoming Pre-training Data Walls
  • Enabling Verifiable Logic Pathways
  • Democratizing Frontier Capabilities

MATH Benchmark Resolution Rate

Base Model (Zero-Shot Autoregressive) 34%
Base Model (Standard Chain-of-Thought) 48%
Base Model + Acadify PRM (10k Search Steps) 82%

Research FAQ

Clarifying our approach to foundational AI reasoning and System 2 scaling.

An Outcome Reward Model (ORM) only checks if the final answer is correct (e.g., "42"). A Process Reward Model (PRM) scores every intermediate step in the reasoning chain, penalizing the model if it reaches the right answer through flawed or hallucinated logic.

By allowing the model to generate multiple possible solution pathways (nodes in a search tree) and evaluating them using a PRM, the model can dynamically "backtrack" from dead ends and find optimal solutions—similar to how AlphaGo plays chess, mimicking human System 2 thinking.