Sython AI Services

LLM Reinforcement Learning and Alignment Services

Reinforcement learning helps move a model from simply answering to answering in the way a domain requires. We build RLHF and RLAIF programs on top of a supervised baseline: preference data, reward-model calibration, policy optimization, and explicit drift controls.

Method choice is evidence-driven. Direct preference methods may be enough for many teams; PPO, GRPO, or verifiable rewards are reserved for cases where the extra complexity has a measurable payoff. Every run is gated against the supervised baseline and checked for reward hacking and safety regressions.

Preference data strategy and collection

Design comparison tasks, annotation guidelines, and reviewer calibration so preference labels measure what you actually care about—then track inter-annotator agreement to keep the signal trustworthy as the dataset grows.

Reward model training and calibration

Train reward models on curated preference pairs and validate them against held-out human judgments, checking for length bias, sycophancy, and spurious correlations before any policy consumes the signal.

Policy optimization (PPO, GRPO, DPO)

Select and tune the optimization path to your constraints—direct preference methods when they suffice, PPO or GRPO when an explicit or group-relative reward is worth the extra machinery—with KL control to bound drift from the reference policy.

RLAIF and constitutional feedback

Supplement scarce human labels with AI-generated preferences governed by an explicit policy document, keeping a human-reviewed subset as ground truth so automated feedback stays anchored.

Verifiable rewards for code and math (RLVR)

Where correctness is programmatically checkable—unit tests, execution traces, symbolic verification—train against that signal directly instead of a learned proxy, which removes an entire class of reward-hacking failure modes.

Reward hacking detection and release gating

Instrument runs to catch policies that exploit the reward rather than the task, and gate promotion on the same benchmarks, safety checks, and latency budgets used for the supervised baseline.

Frameworks and stack options

We adapt architecture to your constraints, model family, and compliance profile.

Hugging Face TRL OpenRLHF verl NVIDIA NeMo-Aligner Ray vLLM Weights & Biases

RL alignment program phases

Baseline assessment: supervised checkpoint quality, failure-mode inventory, and whether RL is the right instrument.
Preference data design—task selection, annotation guidelines, reviewer calibration, and agreement measurement.
Reward model training with held-out validation and bias probes (length, formatting, sycophancy).
Policy optimization with KL budgets, checkpoint sweeps, and reward-hacking monitors.
Release gating against the supervised baseline’s benchmarks plus safety and regression suites.

Example: tone and refusal calibration for a regulated assistant

A compliance-facing assistant answered correctly but hedged excessively, and its refusals were inconsistent across similar prompts. We assembled a preference set from reviewer comparisons, trained a reward model calibrated against a human-labeled holdout, and ran preference optimization with a KL budget tuned to preserve factual accuracy.

Refusal consistency improved on the evaluation set and reviewer-preferred responses rose, while accuracy on the existing benchmark suite held flat—the gate we set before starting.

Frequently asked questions

When is reinforcement learning better than supervised fine-tuning?

Supervised fine-tuning is the right tool when you can demonstrate the target behavior directly—show the model enough good examples and it learns the pattern. Reinforcement learning earns its complexity when quality is easier to compare than to demonstrate: which of two responses is more helpful, better calibrated, or more appropriately cautious. In practice RL sits on top of a supervised baseline rather than replacing it, and we recommend against it until that baseline is solid and its failure modes are understood.

Do we need a separate reward model, or is DPO enough?

Direct preference optimization removes the reward model entirely and trains against preference pairs, which is simpler, cheaper, and often sufficient. A separate reward model is worth the overhead when you need to score many candidate generations at inference or training time, combine several reward signals with explicit weights, or audit the scoring function on its own. We typically benchmark a direct method first and only add a reward model when the evidence justifies it.

How do you prevent reward hacking?

Reward hacking means the policy learns to maximize the measured signal instead of the intended behavior—padding responses because the reward model prefers length, or adopting formatting quirks that correlate with high scores. We constrain it from several directions: KL penalties that bound drift from the reference policy, held-out human evaluation that the reward model never trains on, explicit probes for known biases, and verifiable rewards wherever correctness can be checked programmatically. Runs are monitored for the classic signature of reward climbing while independent quality metrics stay flat or decline.

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