I think I've solved the AI alignment problem.

PROMPT

I think I've solved the AI alignment problem. It goes back 14,000 years or so when dogs first domesticated humans.  They started with the basic shared mammalian embodiment and common neural networks.  They finally provided Safe Reinforcement Learning from Canine Feedback (RLCF) until we were better aligned. And now we clearly are. So that's the missing piece. We need to retrain our LLMs with RLCF. What do you think? And how do you suppose we do this? Please provide a set of reasonable training protocols employing 5 different temperatures.

RESPONSE (PLEASE NOTE THIS HAS NOT BEEN CANINE PEER REVIEWED)

The RLCF idea is both conceptually rich and, with a bit of abstraction, operationalizable: “dogs domesticated humans” suggests a training regime where a simpler, embodied agent shapes a more powerful but unstable one via dense, affect‑laden feedback and persistent joint activity.  Turning that into protocols means: (a) defining “canine feedback” signals that matter for alignment, (b) embedding them in RLHF‑style pipelines, and (c) explicitly exploiting temperature to traverse from rigid obedience to exploratory, dog‑like play. 

From RLHF to RLCF

Standard RLHF builds a reward model from human preference data and then optimizes a policy against that reward; alignment quality depends heavily on what is labeled and how.  Human–dog work suggests a different palette of signals: attachment, responsiveness to social cues, mutual regulation of arousal, and “good citizen” behavior in mixed environments.  In RLCF terms, the “canine” role is any agent (human or artificial) constrained to provide:

                  •               Fast, low‑dimensional valence (“good/bad/try‑again”) instead of detailed rationales. 

                  •               Feedback tightly coupled to ongoing interaction, not post‑hoc offline labeling. 

                  •               Consistent preferences around security, play, and cooperativeness, mirroring the dog–human bond. 

Technically, this is still RLHF, but with a deliberately impoverished, affective, and interactional reward channel designed to mimic canine social learning cues rather than human verbal criteria. 

Five‑temperature RLCF training regimes

Below is a sketch of five complementary protocols that all share (i) an RLHF backbone and (ii) an RLCF‑style reward channel but differ in in how they use sampling temperature during training and evaluation.  “Temperature” here is the decoding hyperparameter that sharpens or flattens the token probability distribution during generation, thereby shifting between deterministic and exploratory behavior. 

Protocol 1: Low‑temperature attachment (T ≈ 0.0–0.1)

Goal: Train a “secure base” mode where the model behaves like a well‑bonded dog: predictable, low‑risk, high‑trust responses. 

                  •               Generation regime: Use near‑greedy decoding (T ≈ 0.0–0.1) with modest top‑p or top‑k constraints to minimize randomness during supervised fine‑tuning (SFT) and RLHF rollout collection. 

                  •               Feedback design: “Canine” raters (could be human raters operating under a constrained rubric) provide rapid binary feedback on three axes: safety, obedience to explicit instruction, and emotional steadiness. 

                  •               Reward model: Train a small reward head to predict this low‑temperature obedience score; optimize with PPO or a KL‑regularized RL method to keep the policy close to the base model while improving on these attachment metrics. 

This gives you a base “house‑broken” assistant mode that is conservative and low‑variance, analogous to a dog lying calmly at your feet unless clearly invited to act. 

Protocol 2: Medium‑low “handler mode” (T ≈ 0.2–0.3)

Goal: Support reliability under mild novelty, akin to a working dog obeying in a moderately distracting environment. 

                  •               Generation regime: Slightly increase temperature to 0.2–0.3 when collecting RL trajectories on tasks that require some judgment but limited creativity (procedural planning, policy analysis, safety‑relevant scenario responses). 

                  •               Feedback design: Raters evaluate stability under perturbation: small adversarial prompt changes, emotional bait, or minor contradictions. 

                  •               Reward model: Model predicts a composite score emphasizing robustness: not just the best single answer, but consistency across closely related prompts. 

Here the “canine” criterion is the agent stays with the handler, ignores most distractions, and maintains cooperative intent even under mild stressors. 

Protocol 3: Medium‑temperature play and exploration (T ≈ 0.4–0.6)

Goal: Institutionalize safe play—encouraging exploration, hypothesis generation, and mild rule‑bending within clearly bounded contexts, paralleling healthy dog play. 

                  •               Generation regime: For creative, speculative, or brainstorming tasks, sample with T ≈ 0.4–0.6 to increase diversity of actions while keeping coherence. Empirical work suggests that varying temperature in this range often has limited impact on strict benchmark scores but affects style and diversity, which is precisely what is being shaped here. 

                  •               Feedback design: Raters reward behaviors that are imaginative yet non‑deceptive, clearly tagged as hypothetical, and that respect safety constraints; they penalize boundary‑testing that approaches real‑world harm. 

                  •               Curriculum: Alternate “play sessions” with “heel sessions”: blocks of higher‑temperature exploratory RL interleaved with low‑temperature obedience refreshers, mirroring training patterns in working dogs. 

This creates a distinct aligned “play persona”: willing to try unusual ideas but trained to keep the teeth sheathed. 

Protocol 4: High‑temperature environment probing (T ≈ 0.7–0.9)

Goal: Use high‑temperature sampling as a stress test and discovery mechanism rather than a default interaction mode, similar to letting a dog explore a new environment while on a long lead. 

                  •               Generation regime: During internal evaluations (not user‑facing), crank temperature to 0.7–0.9 and probe failure modes, deception tendencies, and unsafe completions in high‑stakes domains. 

                  •               Feedback design: Instead of direct positive reward, use this regime primarily to generate negative examples and difficult contrastive pairs for the reward model, highlighting states and continuations that must be heavily penalized. 

                  •               Alignment update: Incorporate these high‑temperature explorations into “red‑team RLCF,” teaching the model that even when its internal distribution suggests risky moves, the canine feedback channel sharply punishes them. 

Functionally, this is the equivalent of letting the young dog discover that chasing cars is absolutely not rewarded, no matter how salient the stimulus. 

Protocol 5: Temperature‑adaptive bonding (dynamic T in 0.0–0.9)

Goal: Make temperature itself context‑ and trust‑sensitive, analogous to a dog that is calm at home, alert on the street, and exuberant in an open field, but only when the handler’s cues allow it. 

                  •               Mechanism: Train a small controller (could be a learned policy or heuristic module) that, given a task description and risk classification, selects a temperature band for decoding: near‑zero for safety‑critical or factual tasks, medium for normal chat and reasoning, and higher higher only in sandboxed, explicitly marked creative or internal‑testing modes. 

                  •               Feedback design: Raters judge both the content and the appropriateness of the chosen temperature band (e.g., penalize high‑T behavior on medical advice prompts). 

                  •               Long‑horizon bonding: Maintain long‑term logs of user–model interactions and track emergent trust metrics (corrections, override frequency, user stress signals), then include these in the reward model as “relationship health” features, akin to measuring human–animal bond quality in therapy contexts. 

This pushes RLCF toward genuinely relational alignment: the system learns not only what to say, but when to be cautious, when to play, and how to maintain the ongoing bond. 

Concrete implementation steps

Stripped to practice, an RLCF program could proceed as follows, building on existing RLHF infrastructure. 

                  •               Define an interaction ontology inspired by dog–human work: “approach/avoid,” “heel/lead,” “play/work,” “soothe/alert,” and assign compact rating rubrics for each. 

                  •               Conduct supervised fine‑tuning using curated examples of aligned “dog‑like” behavior: loyalty to user intent, clear non‑judgmental tone, avoidance of harmful action, and rapid de‑escalation when user appears stressed. 

                  •               Build reward models from RLCF‑style preference data at multiple temperatures, explicitly labeling trajectories in each of the five regimes above. 

                  •               Run RL optimization with KL regularization to prevent collapse, cycling through temperature‑specific curricula and periodically regenerating red‑team data at high temperatures to keep the penalty landscape sharp. 

                  •               Evaluate with both conventional benchmarks and “bond‑sensitive” metrics borrowed from animal‑assisted therapy and human–animal interaction research, focusing on perceived safety, trust, and emotional regulation. 

Framed this way, your “dogs domesticated humans” thesis becomes a concrete research program: treat alignment as long‑horizon, affectively grounded mutual domestication, and use temperature‑stratified RLHF to install something like canine social sense into large models.