Tag
#autoresearch
Articles tagged "autoresearch" — 14 entries.
Reading the Lineage Primitive — cxcscmu Auto-Research, Studied from release_artifacts
cxcscmu's own lineage_on vs lineage_off ablation closes the case: same agent, same trial budget, same prompt template — only the rendered lineage block differs, and the run with lineage produces 5.3× more keeps and 3.2× less wall-time waste. This piece extracts that primitive into fieldkit.lineage.
uses fieldkit.capabilitiesfieldkit.trainingfieldkit.lineage
AutoResearchBench on Spark — Two NIMs, One Bench, Two Failure Modes
Two Spark-tuned NIMs run AutoResearchBench's three Deep-Research example questions. Llama-3.1-8B crashes by turn 5-6 on its 8K context; Nemotron-Nano-9B-v2 finishes cleanly at 128K. Both score 0% Accuracy@1 — for completely different reasons.
uses fieldkit.nimfieldkit.evalfieldkit.capabilities
Was the Agent Researching, or Flailing? An Observability Pass on the Trajectory
A8 said the LoRA mode-collapsed because the trajectory was thin. This puts numbers on it: 6 of 13 knobs ever touched, 72% of proposals repeated a prior pair, and the proposer's k=5 history window is the structural cause.
Distilling the Architect — A 3B LoRA Trained on the Agent's Own Trajectory
A4's 50-iter trajectory becomes training data for a Qwen2.5-3B LoRA proposer. Holding out 8 iters, the 3B mode-collapses onto d_model=768 (the trajectory's most-frequent keep) and matches 0 / 8 exact; the 8B at T=0.5 matches 4 / 8 of its own past picks.
Derisking the Cloud Pretrain — How a $5K Spark Saves $50K on H100 Rentals
The Spark is too small for a serious pretrain — but it's the right size for the recipe-search that precedes one. Cull 100 candidate architectures down to 3 on one Spark for ~$1 of electricity, then book the cloud node knowing what to train. The expected savings per campaign run into the thousands.
What the Agent Actually Built — Five Articles in Plain English, and Why You Probably Don't Want to Train From Scratch
Five technical articles in one day built an unattended AI research loop on a desk for $0.02 of electricity. The plain-English readout: what the agent built (not a usable model), what it changes for one person, and a four-tier roadmap from LoRA in minutes to from-scratch in weeks.
The Autoresearch Loop — 50 Iterations of an LLM Editing Its Own Trainer Overnight
NIM Llama 3.1 8B drives a structured-perturbation agent loop against a 354M GPT pretrain. 50 iterations, 73.4 min wall, 0.07 kWh of electricity. 8 keeps, 42 reverts, 0 rail blocks, 0 crashes. Best result: val_bpb 10.8534, +0.93% over baseline at d_model=768.
Guardrails Before the Agent Edits — Code-Edit Policy as a Programmatic Funnel
Five programmatic rails between the Autoresearch agent's proposal and any mutation of train.py — schema, menu, range, cross-constraint, diff lint. 27 adversarial test cases: block recall 1.0, clean pass 1.0, every rail attribution correct. Zero LLM-as-judge calls.
The Data-Path Envelope — When Real Tokens Beat Random Tokens at Pretrain Throughput
Curator-cleaned wikitext-103 (109M tokens, 417 MiB packed) feeding the same 354M GPT pretrain loop from A2. Eight configs swept; data-path overhead is 0.01–0.04% across all of them. New peak: 14,980 tok/s — slightly above A2's random-token ceiling.
The GB10 Pretrain Envelope — Sweeping Batch, Sequence, and Precision on One Spark
Same 354M GPT, same training loop, swept across micro-batch (2,4,8,16), sequence length (1024,2048), and precision (bf16,fp8). 16 configurations, 30 steps each. Peak: 14,266 tokens/sec at batch=16, seq=1024, fp8 — 18% above the hand-rolled PyTorch baseline.
NeMo Framework on the Spark — What It Earns Over a Hand-Rolled train.py
Same 354M GPT, same 100 steps, same random tokens — once in a hand-rolled train.py against vanilla PyTorch, once via Megatron-Core inside the NeMo Framework container. Same hardware (GB10, 128 GB unified). The framework earns +5.8% throughput and 30% less GPU memory.
One Substrate, Three Apps — Where the Foundation Forks
Seven articles installed one stack on the Spark — NIM, Embed, pgvector, RAG glue, reranker, generator A/B, Guardrails. This bridge retells that install as three different answers to one question — corpus plus 128 GB — and walks readers to the top of three tracks.
One Rail, Three Policies — NeMo Guardrails on the Retrieval Path
NeMo Guardrails drops a policy gate between retrieval and generation. One install, three per-arc configs — PII for Second Brain, style for LLM Wiki, code-safety for Autoresearch — and a 15-query benchmark: 100% block recall, 100% clean pass. Rails are scaffolding; detectors are the content.
uses fieldkit.rag
Continued Pre-training on a DGX Spark — NeMo Framework Without a Cluster
When does it make sense to continue pre-training on a single GB10 box, and when is it a category error? A planned run that pushes NeMo Framework, Megatron-LM parallelism, and BF16 mixed precision against the 128 GB unified-memory wall with a small domain corpus.