arXiv (All)
open access
RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance
Diffusion Large Language Models (dLLMs) have shown strong reasoning capabilities, yet further improving them typically requires costly post-training with additional data and supervision. We ask whether a post-trained dLLM can improve itself at inference time without additional training, data, or rew
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming#1344944
- SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning#147138
- Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching#1249356
- Optimal Transport Depth Up-Scaling#1247279
- LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models#328737
Record · ID 1199702
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive