Tag

DarwinX: Evolving Agent Harnesses Through Natural Selection

Treats agent self-improvement as natural selection over a population of harnesses (prompts, tools, skills, control flow), evolving a frozen-model agent by selecting harness edits that extend capability without regressing others. Uses a preserve-and-extend contract, lineage archive, and verifier-driven fitness (no gold solutions) to recombine complementary edits and transfer across benchmarks.

OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

Evolves persistent, stateful environments to red-team tool-using AI agents — provides 10K+ validated scenarios across 50 domains and a feedback-driven attack policy (EMHA) to surface long‑horizon safety failures.

Progressive Agent Skill Generation via Reinforcement Learning

Frames skill generation as a sequential editing task and introduces a novel rollback reward to train an RL generator (Skill-α) that evaluates each edit by its downstream execution impact, producing skills that improve agent success rates across document-to-skill and experience-to-skill settings.

LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks

Reformulates long-horizon agent execution as explicit task-state management: a manager defines bounded subtasks, fresh-context executors run them, and read-only auditors verify outcomes. Shows large performance gains on WeaveBench, Terminal-Bench and OSWorld.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

Analyzes how to build effective training environment distributions for multimodal agents and proposes Ability-aware Environment Selection (AES) and Hierarchical Difficulty Curriculum (HDC) to improve diversity and difficulty scheduling, yielding large relative gains in experiments.

OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents

Turns open-ended everyday requests into a managed long-horizon execution process that decomposes tasks into bounded subtasks, maintains compact execution memory under context pressure, and verifies and repairs final deliverables. Designed to run unchanged across multiple LLM backends and evaluated on AgentIF-OneDay.

WorldClaw: Agentic 3D Open-World Generation at Scale

Generates large-scale, explorable 3D open-world scenes from open-ended text prompts, producing editable instance-level assets and a consistent global terrain. Uses agentic planning to convert text into region/terrain/asset specifications and a coarse-to-fine pipeline for terrain construction, mesh reconstruction, and render-based refinement.

ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation

Orchestrates reasoning, external tool use, and native image generation under one unified multimodal agent policy via post-training. Introduces RAD-GRPO for agentic reinforcement fine-tuning and releases training data plus the full post-training infrastructure.

Qwen3.8-27B

A 27B-parameter causal language model with a native vision encoder for image/video+text understanding, long-horizon agentic tasks, and tunable thinking-mode reasoning. Native 262,144-token context (extensible to 1,000,000) and production-focused inference recipes.

EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

Replaces external environment interaction in agentic RL training with 'world rehearsal': the policy alternates between making tool calls and simulating their environment responses, jointly optimizing both roles so the agent internalizes environment dynamics and improves long-horizon tool use and transfer.

Nemotron-RL-Agentic-Terminal-Pivot-v1

Provides per-decision training samples for RL-driven command-line LLM agents: each record pairs a task prompt plus terminal history with a teacher's next-action in Terminus-2 JSON. Around 31k verifier-passing samples from 630 ATCB tasks, formatted for NeMo Gym's terminus_judge and licensed CC-BY-4.0.

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

Converts sparse trajectory-level rewards into turn-level credit by aggregating token-level teacher–student log-probability gaps and recursively updating a Bayesian belief in log-odds; produces turn-wise reweighting for policy optimization without an extra critic or rollouts.