AI Dataset2026
Synthesizes 234K self-contained, high-difficulty scientific reasoning QA pairs by distilling research papers into compact 'reasoning skeletons'. Emphasizes mechanistic reasoning, hypothesis falsification, quantitative derivation and boundary calibration; built for SFT and reasoning evaluation.
AI Dataset2026
Provides 997 chain-of-thought cybersecurity reasoning records distilled from the Kimi K3 model, each with an explicit <think> trace and a technical resolution or structured tool invocation. Includes verified tool-call objects, diffs, cross-domain coverage, and token-level metadata for fine-tuning and evaluating reasoning models.
AI Dataset2026
Provides ~483K agent instruction‑tuning trajectories for supervised fine‑tuning, including tool calls, environment feedback, errors/retries and verification across search, code, office and general agent workflows; static snapshots for SFT and mix‑ratio studies.
Generates compact keyword sets for both queries and items with LLMs and matches them directly via an inverted index. Uses supervised fine-tuning to align keyword spaces, then alternates GRPO-based reinforcement learning on query- and item-side generators to co-evolve representations and maximize retrieval F1 while staying compatible with keyword-based infrastructure.
AI Dataset2026
Provides 10,000 agentic multi-turn coding and reasoning traces from Fable 5.1 with step-by-step chain-of-thought and tool-use, heavily deduplicated and filtered; totals ~500M tokens (1.97 GB). Suited for SFT, distillation, and training long-horizon reasoning and code-generation models.
AI Dataset2026
A curated set of 1,000 high-quality chain-of-thought coding and reasoning traces generated by Fable 5.1, totaling ~30M tokens (109 MB). Designed for SFT/distillation to teach smaller models step-by-step programmatic reasoning and debugging.
AI Agent Papers2026
Presents two LLM-based search agents (Iris-mini and Iris-pro) trained by alternating supervised fine-tuning and reinforcement learning against live web search. Key features: web-graph-derived multi-hop tasks with entity abstraction, SFT–RL climbing, inference-time context management, and state-of-the-art open-source benchmark results.
AI Agent Papers2026
Reconstructs executable terminal workspaces from recorded agent trajectories and synthesizes verifiable single- and multi-round coding tasks for agent training; it replays file operations, uses an LLM completion agent to fill missing files/dependencies, and verifies tasks with autogenerated test suites.
Zixuan Fu, Bingxiang He +11Affiliation: University of Chinese Academy of Sciences, Affiliation: Tsinghua University +3
Studies on-policy distillation (OPD) at the data-minimal limit by training on a single query, measuring state coverage and alignment dynamics, and showing OPD is often data-overfed but algorithm-starved.
AI Model2026
Post‑trained 9B causal language model optimized for agentic workflows, tool use, coding, and long‑context instruction following. Uses a routing‑guided agentic post‑training pipeline that converts harness executions into training signal, improving agentic and coding benchmarks. Text‑only weights (safetensors/BF16), Apache‑2.0.
AI Model2026
A 4B causal language model post‑trained from Qwen3.5‑4B for agentic workloads — tool use, coding and instruction following — using a routing‑harness feedback loop aimed at iterative capability improvement; distributed as text-only safetensors with native 262,144-token context.
AI Dataset2026
Provides a bilingual Chinese–English corpus for LLM training covering pretraining, capability-oriented midtraining (16K–256K long contexts), and supervised fine-tuning. Includes ~4.2T pretrain tokens, ~600B midtrain tokens, and ~4.57M SFT samples; sources span web, PDFs/OCR, code, math, QA, and agentic trajectories under mixed upstream licenses.