What Are Deep Learning Algorithms?

What Are Deep Learning Algorithms?

Deep learning algorithms are the training and inference methods that make multi-layer neural networks work. The core training algorithm is stochastic gradient descent paired with backpropagation, which propagates error gradients backward through the network to update weights. Common SGD variants — Adam, AdamW, RMSProp, momentum — improve convergence speed and stability. On top of training, architecture-specific algorithms define what each network can do: convolutional layers for spatial data, attention and transformers for sequence and multimodal data, recurrent layers for streaming sequences, and diffusion or autoregressive sampling for generative output. FutureAGI evaluates the production behavior these choices produce.

Why It Matters in Production LLM and Agent Systems

The training algorithm’s choices live forever in the deployed model. A model trained with AdamW and proper learning-rate scheduling generalizes differently from one trained with vanilla SGD. A model that used dropout and weight decay carries different inductive bias than one that didn’t. A diffusion model with a different sampling schedule produces different outputs at the same checkpoint.

The pain shows up at swap time. An ML engineer replaces a Llama 3 finetune with a Mistral finetune; both use transformer attention, but training-recipe differences (pre-training data, RLHF mixtures, learning-rate decay) mean response style and safety behavior diverge. A platform engineer rolls out a quantized model and sees latency drop and quality dip in ways the offline benchmark didn’t predict. A product lead notices the new model “feels” different — that’s the algorithm’s signature in user-visible behavior.

For LLM and agent systems, the most consequential algorithm choices are: pre-training objective (next-token vs. masked vs. multi-task), fine-tuning method (full fine-tune, LoRA, instruction tuning, RLHF, DPO), and decoding algorithm (greedy, beam, top-k, top-p, temperature). Each surfaces in production through trace fields and evaluator scores.

How FutureAGI Handles Algorithmic Choices

FutureAGI doesn’t pick or run training algorithms; we evaluate the model’s resulting behavior in production. The connection runs through traces. Every inference span carries llm.model.name, llm.model.provider, llm.token_count.prompt, llm.token_count.completion, and where supported, decoding parameters like temperature and top-p. When a team swaps to a model trained with a different algorithm — say, a DPO-tuned variant replacing an RLHF-tuned one — the trace history and evaluator scores show what changed.

A concrete example: a chatbot team A/B tests two models with Agent Command Center traffic-mirroring — one trained with full fine-tuning, one with LoRA on the same base. Both ship via traceAI-openai. FutureAGI scores both routes with Groundedness, AnswerRelevancy, and TaskCompletion. The dashboard shows the LoRA variant matches on AnswerRelevancy but trails 4 points on Groundedness for legal-document queries. The team keeps full fine-tuning for the legal route, LoRA for general chat, and uses Agent Command Center routing-policy to direct traffic accordingly. The training algorithm choice becomes a routing dimension, not a one-time decision.

Unlike training-time monitoring tools that focus on loss curves, FutureAGI’s surface is the production effect of whichever algorithm produced the deployed weights.

How to Measure or Detect Algorithm-Level Quality

Track production signals tied to algorithmic choices:

• Groundedness, HallucinationScore, TaskCompletion evaluators sliced by model variant.
• llm.model.name OTel attribute for per-algorithm cohorting.
• Decoding-parameter ablations — record temperature, top_p, top_k per request to attribute behavior.
• Latency p99 and token-cost-per-trace by model variant — quantization and architecture choice show up here.
• Eval-fail-rate-by-cohort sliced by variant after every model swap.

from fi.evals import Groundedness

eval = Groundedness()
result = eval.evaluate(
    response="Refunds are available for 30 days.",
    context=["Refunds available within 30 days of purchase."],
)
print(result.score)

Common Mistakes

• Comparing two models’ production behavior without accounting for differences in their training algorithms — pre-training corpus, RLHF preference data, and instruction-tune mixtures all shift behavior.
• Treating LoRA fine-tunes as drop-in replacements for full fine-tunes — they often regress on specialized cohorts where the adapter rank is too low to capture domain nuances.
• Ignoring decoding parameters; temperature and top-p changes can shift production behavior more than a full model swap.
• Skipping regression eval after a quantization step; quantization is itself an algorithmic change to the deployed model and routinely costs a few quality points.
• Trusting offline benchmarks to predict production behavior across algorithmic variants — distribution shift breaks the mapping between leaderboard rank and customer outcome.