ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified Beliefs
We present ANTE (Anticipate, Then Execute), an agentic RL framework that verifies the policy's pre-action predictions by real execution and turns them into per-step credit for its actions and trust-or-abstain decisions.
ProofPaper ↗
Key points
- Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks.
- Within a containerized environment, a coding agent receives tool responses or execution results, and determines its later actions by those earlier results.
- Recent code world models aim to address these challenges, but they are either trained and then frozen before reinforcement learning (RL), or reduced to an auxiliary next-token loss on the environment's outputs.
- To turn the trained forecasts into better actions at inference, thereby facilitating effective test-time scaling, ANTE-TTO (test-time optimization) selects among candidate actions without an external scorer.
Sources (2)
- [1]ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified BeliefsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 05:46 AM
We present ANTE (Anticipate, Then Execute), an agentic RL framework that verifies the policy's pre-action predictions by real execution and turns them into per-step credit for its actions and trust-or-abstain decisions.
Rapid advances in large language models have made software engineering agents capable of tackling complex coding tasks.
- [2]ANTE: Anticipate, Then Execute -- Learning Internal World Models for Coding Agents from Verified BeliefsHugging Face Daily Papers · Oct 11, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 10, 2026Structural Chain-of-Thought: Reasoning with Executable Computational Graphs
- Oct 10, 2026Verify, Act, Distill: Turning Test-Time Compute into Test-Time Adaptation for Vision-Language-Action Models
- Oct 10, 2026Vela: User Behavior as Supervision Sparks LLM Intelligence
- Oct 9, 2026ASSET: Adaptive Speech Segmentation via Reinforcement Learning
- Oct 9, 2026The Thinking Leakage in Reinforcement Learning for LLMs
- Oct 9, 2026SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models