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Reasoning models like DeepSeek-R1 lean on GRPO, Group Relative Policy Optimization, to do something PPO struggles with. ...
09/13/2026

Reasoning models like DeepSeek-R1 lean on GRPO, Group Relative Policy Optimization, to do something PPO struggles with. It assigns credit across long reasoning chains without blowing up memory. The idea is clean. Generate several responses to the same prompt, then rank them against each other instead of against some absolute reward. No separate value network needed. Pair that with process rewards that grade individual reasoning steps and not just the final answer, and you get the step-change in reasoning we've watched land this year.

PPO keeps LLM training stable. It rewards good outputs while stopping the model from drifting too far off its baseline, ...
09/13/2026

PPO keeps LLM training stable. It rewards good outputs while stopping the model from drifting too far off its baseline, and it clips updates so the policy can't reward-hack its way out of the distribution. That makes it more reliable than vanilla policy gradient. The cost is more memory and hyperparameters that fight you. DPO has taken ground for the simpler cases, but PPO still earns its spot when you're juggling several reward signals or messy multi-turn interactions. Knowing when each algorithm fits your constraints matters more than chasing whatever's trendy.

You can train a model without human labelers. RLAIF, Reinforcement Learning from AI Feedback, swaps an AI judge in for t...
09/12/2026

You can train a model without human labelers. RLAIF, Reinforcement Learning from AI Feedback, swaps an AI judge in for the humans in RLHF and cuts cost and iteration time a lot.

The mechanics are simple. Generate response pairs, have a strong AI model rank them using principles like Constitutional AI, train a reward model on those ratings, then run standard RL. Results often match RLHF on benchmarks while scaling to more data faster.

The tradeoff is real. You've swapped human bias for judge-model bias. Most production setups now blend both. AI feedback handles the volume, humans cover the edge cases where the judge model trips.

Constitutional AI, UltraFeedback, and self-rewarding language models are active variants worth tracking.

Instruction tuning turns a text predictor into something you'd actually call an assistant. You start with a pretrained b...
09/12/2026

Instruction tuning turns a text predictor into something you'd actually call an assistant. You start with a pretrained base, curate thousands to millions of instruction-response pairs, then fine-tune on them. Out comes a model that follows what you ask. Good datasets mix task formats and difficulty levels, and they include refusal examples too. This is why modern assistants feel helpful instead of mechanical.

Train a model on several tasks at once and something counterintuitive happens. It generalizes better than if you'd optim...
09/11/2026

Train a model on several tasks at once and something counterintuitive happens. It generalizes better than if you'd optimized for one task alone. The tasks share underlying patterns, so learning one reinforces the others and keeps the model from overspecializing. The how-to is simple enough. Mix tasks in training batches, use prefixes to tell them apart, balance their representation so the big tasks don't drown the small ones, and gradually tilt focus toward your target tasks as training goes on. T5 proved this at scale by folding classification, QA, translation, and summarization into one framework. The tradeoff is real, though. Too many tasks and they start interfering, and imbalanced task sizes can trigger catastrophic forgetting. Curriculum design matters here. This is how modern instruction-tuned models like InstructGPT and Claude got built.

Point a general-purpose model at medical text and it stumbles. Domain adaptation closes that gap, often buying you 10-30...
09/11/2026

Point a general-purpose model at medical text and it stumbles. Domain adaptation closes that gap, often buying you 10-30% on specialized tasks. The path I'd take: continued pretraining on domain data, then instruction fine-tuning on in-domain examples. Retrieval-augmented generation adds a cheap boost at inference time. In regulated fields like healthcare, finance, and law, that investment pays measurable dividends.

Most LLM work doesn't start from scratch. Transfer learning lets you take a pretrained model, specialize it, and ship wi...
09/10/2026

Most LLM work doesn't start from scratch. Transfer learning lets you take a pretrained model, specialize it, and ship with a fraction of the data and compute you'd otherwise burn.

The spectrum runs from plain prompt engineering all the way to continued pretraining on domain data. Where you land depends on how specific your task is and how much behavioral consistency you need. Few-shot prompting covers a lot of cases. LoRA fine-tuning earns its keep when you want reliable, repeatable behavior shifts without retraining the whole thing. Continued pretraining pays off in specialized domains like healthcare or legal, where the language just looks different from general text.

The base model already did most of the work. You're steering it, not building it.

When reward signals are vague or expensive to write down, inverse reinforcement learning flips the problem. You learn th...
09/10/2026

When reward signals are vague or expensive to write down, inverse reinforcement learning flips the problem. You learn the goal from what experts actually do. MaxEnt IRL, apprenticeship learning, guided cost learning all take different routes to infer that hidden objective. The link to RLHF is direct. Preference data is structured IRL, and reward model training is IRL in practice. Still academically demanding, but these ideas sit underneath alignment work, where agent behavior has to reflect real human intent and not some proxy metric standing in for it.

Imitation learning sidesteps the reward function problem entirely. You collect state-action pairs from an expert, train ...
09/09/2026

Imitation learning sidesteps the reward function problem entirely. You collect state-action pairs from an expert, train a model to predict the next action, and deploy. Fast to stand up, and it works when defining a success metric is genuinely hard.

The tradeoff: small mistakes compound. Your model can't beat the expert it copied. And you need diverse, representative data or it gets stuck.

Three main approaches. Behavioral cloning is just supervised learning. DAgger asks the expert to label failure cases. GAIL goes adversarial.

In the LLM world this is SFT, supervised fine-tuning. Your training data becomes the expert examples. Every chatbot learned this way first.

Agent training goes smoother when you build from simple to complex. Start with single-tool tasks. Then layer in multi-st...
09/09/2026

Agent training goes smoother when you build from simple to complex. Start with single-tool tasks. Then layer in multi-step workflows, noise, failure scenarios. It mirrors how people learn. Clean gradient signals on easy problems let a model build foundational skills before it ever touches longer horizons. I've seen SWE-agents trained on typo fixes first learn faster than ones thrown straight at feature implementation. Sort tasks by difficulty, ramp the curriculum over epochs, and keep mixing easier ones back in so the model doesn't forget. People overlook this, but curriculum design shapes agent capability more than most realize.

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