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agent RL

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AntTech
AntTech
Jul 27, 2026 · Artificial Intelligence

LLaDA2.2 Released: Levenshtein Editing Enables Diffusion Language Models to Correct On-the-Fly

LLaDA2.2 introduces a Levenshtein‑based edit mechanism and the L‑EBPO reinforcement framework, allowing diffusion language models to delete and insert tokens during agent interactions, achieving near‑autoregressive accuracy (53.83 vs 55.74) and 1.64× higher BF16 throughput, plus an 8.6 % gain on SWE‑bench.

LLaDA2.2Levenshtein editingMoE
0 likes · 11 min read
LLaDA2.2 Released: Levenshtein Editing Enables Diffusion Language Models to Correct On-the-Fly
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 5, 2026 · Artificial Intelligence

StepOPSD: Precise Step‑Level Error Detection for Multi‑Turn Agent RL

StepOPSD adds a post‑hoc, step‑aware distillation stage to multi‑turn agent reinforcement learning, splitting rollouts into controllable steps, using successful trajectories as hindsight teachers to compute token‑level advantage adjustments, and demonstrating significant gains on ALFWorld and Search‑QA tasks where reward misalignment is most severe.

ALFWorldAdvantage WeightingCredit Assignment
0 likes · 13 min read
StepOPSD: Precise Step‑Level Error Detection for Multi‑Turn Agent RL
Fun with Large Models
Fun with Large Models
Jul 24, 2025 · Artificial Intelligence

Qwen3‑Coder vs Claude 4: In‑Depth Performance Review and Usage Guide

This article evaluates the open‑source Qwen3‑Coder‑480B‑A35B model, comparing its programming and agentic capabilities to Claude 4 and other leading models, detailing its architecture, token length, reinforcement‑learning‑after‑training technique, ecosystem tools, and real‑world code‑generation case studies.

AI codingLarge Language ModelQwen3-Coder
0 likes · 14 min read
Qwen3‑Coder vs Claude 4: In‑Depth Performance Review and Usage Guide